Search results “Data mining research problems in chemistry”
Student's t-test
Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php
Views: 387675 Bozeman Science
Data Mining - Predicting Scientific Impact | lectures On-Demand
Avishay Livne - Graduate Student, Computer Science and Engineering at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
O'Reilly Webcast: How to Develop Language Annotations for Machine Learning Algorithms
Text-based data mining and information extraction systems that make use of machine learning techniques require annotated datasets for training the algorithms. In this webcast presented by James Pustejovsky and Amber Stubbs, we will discuss the steps involved in creating your own training corpus for such machine learning algorithms. We walk you through: The annotation cycle Selecting an annotation task Creating the annotation specification Designing the guidelines Creating a "gold standard" corpus Beginning the actual data creation with the annotation process We then mention the most relevant machine learning algorithms for natural language data and tasks, and provide hints for how to choose the right one for your learning task and your own dataset. Finally, we discuss testing and evaluation of the algorithm, along with suggestions for how to revise your system depending on the resulting performance. This is a unique, up-close, step-by-step look at the entire development cycle for NLP system design, from your initial idea, to spec, through annotation and corpus development, to training and testing your algorithm. Don't miss this informative webcast. About James Pustejovsky James Pustejovsky holds the TJX/Felberg Chair in Computer Science at Brandeis University, where he directs the Lab for Linguistics and Computation, and chairs both the Program in Language and Linguistics and the Computational Linguistics MA Program. He has conducted research in computational linguistics, AI, lexical semantics, temporal reasoning, and corpus linguistics and language annotation. He is currently head of a working group within ISO/TC37/SC4 to develop a Semantic Annotation Framework, and is the author of the recently approved ISO specification for time annotation (SemAF-Time, ISO-TimeML) and the draft specification for space annotation (SemAF-Space, ISO-Space). Pustejovsky was PI of a large NSF-funded effort, "Towards a Comprehensive Linguistic Annotation of Language," that involved merging several diverse linguistic annotations (PropBank, NomBank, the Discourse Treebank, TimeBank, and Opinion Corpus) into a unified representation. Currently, he is Co-PI of a major project funded by the NSF to address interoperability for NLP data and tools. He has taught computational linguistics to both graduates and undergraduates for 20 years, and corpus linguistics for eight years. http://twitter.com/jamespusto About Amber Stubbs Amber Stubbs recently completed her Ph.D. in Computer Science at Brandeis University, and is currently a Postdoctoral Associate at SUNY Albany. Her dissertation focused on creating an annotation methodology to aid in extracting high-level information from natural language files, particularly biomedical texts. Her website can be found at http://pages.cs.brandeis.edu/~astubbs/ Produced by: Yasmina Greco
Views: 2769 O'Reilly
Decision Tree Tutorial in 7 minutes with Decision Tree Analysis & Decision Tree Example (Basic)
Clicked here http://www.MBAbullshit.com/ and OMG wow! I'm SHOCKED how easy.. No wonder others goin crazy sharing this??? Share it with your other friends too! Fun MBAbullshit.com is filled with easy quick video tutorial reviews on topics for MBA, BBA, and business college students on lots of topics from Finance or Financial Management, Quantitative Analysis, Managerial Economics, Strategic Management, Accounting, and many others. Cut through the bullshit to understand MBA!(Coming soon!) http://www.youtube.com/watch?v=a5yWr1hr6QY
Views: 515526 MBAbullshitDotCom
Sampling & its 8 Types: Research Methodology
Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm
Views: 266895 Examrace
Carles Bo (ICIQ) - Taming the Big Data in Computational Chemistry (4 Feb 2015)
The massive use of simulation techniques in chemical research generates huge amounts of information, which starts to become recognized as the BigData problem. The main obstacle for managing big information volumes is its storage in such a way that facilitates data mining as a strategy to optimize the processes that enable scientists to face the challenges of the new sustainable society based on the knowledge and the rational use of existent resources. The present project aims at creating a platform of services in the cloud to manage computational chemistry. As other related projects, the concepts underlying our platform rely on well defined standards and it implements treatment, hierarchical storage and data recovery tools to facilitate data mining of the Theoretical and Computational Chemistry's BigData. Its main goal is the creation of new methodological strategies that promote an optimal reuse of results and accumulated knowledge and enhances daily researchers’ productivity. This proposal automatizes relevant data extracting processes and transforms numerical data into labelled data in a database. This platform provides tools for the researcher in order to validate, enrich, publish and share information, and tools in the cloud to access and visualize data. Other tools permit creation of reaction energy profile plots by combining data of a set of molecular entities, or automatic creation of Supporting Information files, for instance. The final goal is to build a new reference tool in computational chemistry research, bibliography management and services to third parties. Potential users include computational chemistry research groups worldwide, university libraries and related services, and high performance supercomputer centers.
Views: 108 Info HPCNow!
Tentative steps towards mining PhD theses
Sara Gould, Development Manager, British Library Sara will talk about the Library’s recent participation in a national project to mine chemical compounds from the pages of PhD theses, describe some of the challenges in accessing theses for Text and Data Mining, and invite participants to ‘have a go’ at mining theses for new research purposes.
CAREERS IN CHEMISTRY– Degree,Healthcare Scientist,Engineering firms,Laboratory Jobs
CAREERS IN CHEMISTRY.Go through the career opportunities of CHEMISTRY, Govt jobs and Employment News channel from Freshersworld.com – The No.1 job portal for freshers in India. Visit http://www.freshersworld.com?src=Youtube for detailed Career information,Job Opportunities,Education details of CHEMISTRY. Chemistry as a subject provides insight about topics of concern that deal with the composition, properties, structure and most importantly, change of matter. When you first listen to the word chemistry, all you can think of is molecules and matters and structures. Chemistry as a subject paves way for much more than any of these mentioned. There is a major assumption that 'All chemists wear white coats'. In fact, it is the other way round! Here is a list of jobs that a student can attain to after having his/ her chemistry degree in hand: • Healthcare scientist, clinical biochemistry • Forensic scientist • Pharmacologist • Analytical chemist • Research scientist (physical sciences) • Toxicologist • Chemical engineer Also, there are a few options wherein this degree could be used in a more specific way: • Science writer • Higher education lecturer • Patent attorney • Chartered certified accountant • Environmental consultant • Secondary school teacher The skills which are absolutely required for a student having a chemistry degree in hand is to whilst bagging a job in need are: • analysis and problem-solving; • research skills • technical skills • quantitative skills • organizational skills • time management and organization; • written and oral communication; • monitoring/maintaining records and data; • teamwork; • IT and technology. Here is a list to as in who all employs chemistry graduates that would rather provide an insight to the career as such: • Consulting firms • Pharmaceutical companies • Museums • Engineering firms • Industrial inspection firms • Magazines and newspapers Cosmetics and fragrance production companies • Computers and telecommunications Government agencies • Fine and heavy chemical manufacturing companies • Food and beverage production companies • Mining and metallurgy companies • Law Firms • Oil and gas companies • Plastic manufacturing companies • Universities, colleges and schools • Hospitals & other medical organizations • Pulp and paper companies • Environment and pollution control firms Here is a list of colleges mastering in chemistry: Osmania PG College, Kurnool M S R S Siddardha Degree College Gayatri College of Science & Management Government Degree College (Men) Ramakrishna Mission Vivekananda College J & J College of Science Sheth L H Science College M G Science Institute Bhavan's R A College of Science Sir C R Reddy Autonomous College NIT Silchar NIT Surathkal NIT Rourkela NIT Jalandhar NIT Durgapur NIT Agartala Meenakshi College for Women Ethiraj College for Women Sri Venkateswara College St. Stephen’s College Hans Raj College Ramjas College Miranda House College Gargi College BITS Pilani MNIT Jaipur IIT Kharagpur IIT Bombay IIT Delhi IIT Guwahati IIT Kanpur IIT Madras NIT Warangal NIT Trichy KMC Delhi Acharya Narendra Dev College ISM Dhanbad IIT Roorkee There are thousands of ways in getting into a chemistry job. All you need to do is have patience and focus on the right direction. Patience and perseverance are the two roads to success, especially in chemistry. For more jobs & career information and daily job alerts, subscribe to our channel and support us. You can also install our Mobile app for govt jobs for getting regular notifications on your mobile. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com?src=Youtube - - ***Disclaimer: This is just a career guidance video for fresher candidates. The name, logo and properties mentioned in the video are proprietary property of the respective companies. The career and job information mentioned are an indicative generalised information. In no way Freshersworld.com, indulges into direct or indirect recruitment process of the respective companies.
Healthcare Data Mining with Matrix Models (Part 1)
Authors: Joel Dudley, Icahn School of Medicine at Mount Sinai Ping Zhang, IBM Thomas J. Watson Research Center Fei Wang, Department of Healthcare Policy and Research, Cornell University Abstract: In the last decade, advances in high-throughput technologies, growth of clinical data warehouses, and rapid accumulation of biomedical knowledge provided unprecedented opportunities and challenges to researchers in biomedical informatics. One distinct solution, to efficiently conduct big data analytics for biomedical problems, is the application of matrix computation and factorization methods such as non-negative matrix factorization, joint matrix factorization, tensor factorization. Compared to probabilistic and information theoretic approaches, matrix-based methods are fast, easy to understand and implement. In this tutorial, we provide a review of recent advances in algorithms and methods using matrix and their potential applications in biomedical informatics. We survey various related articles from data mining venues as well as from biomedical informatics venues to share with the audience key problems and trends in matrix computation research, with different novel applications such as drug repositioning, personalized medicine, and electronic phenotyping. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 634 KDD2016 video
Deep Learning for Science
In this video from the 2017 Intel HPC Developer Conference, Prabhat from NERSC and Michael F. Wehner from LBNL present: Deep Learning for Science. "Deep Learning has revolutionized the fields of computer vision, speech recognition and control systems. Can Deep Learning (DL) work for scientific problems? This talk will explore a variety of Lawrence Berkeley National Laboratory’s applications that are currently benefiting from DL. We will review classification and regression problems in astronomy, cosmology, neuroscience, genomics and high-energy physics. We will share results from a deep-dive into the problem of detecting extreme weather patterns in climate simulations. Lastly, we will conclude with short and long-term challenges at the frontier of DL research, and speculate about the role of DL and AI in the future of scientific discovery." Prabhat leads the Data and Analytics Services team at NERSC. In this role, he is responsible for deploying the Big Data stack on NERSC platforms, spanning capability areas in Data Analytics, Management, Workflows, Visualization, Transfer and Access. Prabhat is the Director of the Big Data Center at NERSC, which is enabling capability Data applications to run on the Cori supercomputer. Prabhat’s current research interests span Deep Learning, Machine Learning and Applied Statistics. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley. Michael F. Wehner is a senior staff scientist in the Computational Research Division at the Lawrence Berkeley National Laboratory. Dr. Wehner’s current research concerns the behavior of extreme weather events in a changing climate, especially heat waves, intense precipitation, drought and tropical cyclones. Before joining the Berkeley Lab in 2002, Wehner was an analyst at the Lawrence Livermore National Laboratory in the Program for Climate Modeling Diagnosis and Intercomparison. He is the author or co-author of over 165 scientific papers and reports. He was a lead author for both the 2013 Fifth Assessment Report of the Intergovernmental Panel on Climate Change and the 2nd,3rd and 4th US National Assessments on climate change. Dr. Wehner earned his master’s degree and Ph.D. in nuclear engineering from the University of Wisconsin-Madison, and his bachelor’s degree in Physics from the University of Delaware. Learn more: https://www.intel.com/content/www/us/en/events/hpcdevcon/overview.html Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Views: 459 RichReport
Providing insight into the structure of scientific papers
How is a scientific paper structured and how related is it to other papers? These are some of the things that Iana Atanassova of the University of Bourgogne Franche-Comte (Besancon, France) focuses on in her research. She uses text and data mining to study full-text scientific articles. Studying these papers can be a challenge, as they are usually in a format that is hard to process. For more info about text and data mining, visit www.openminted.eu
Views: 148 OpenMinTeD
Deb Grubbe on Big Data in Chemical Engineering
Chemical Engineer Deb Grubbe had an opportunity to chat with CEP senior editor Michelle Bryner about how Big Data could be used to improve safety in the chemical process industry (CPI), in turn improving the health of the industry by reducing incidents and downtime, and making companies more profitable. Using Big Data tools to analyze volumes of process data, laboratory data, data related to structures, or electrical systems can provide engineers a much deeper understanding of their processes, which they can then use to prevent problems before they happen.
Views: 1232 AIChE ChEnected
Saturday Science at Scripps Research: Markers Magnets and Microbiomes: Mining Biology with Chemistry
(Visit: http://www.uctv.tv/) The Scripps Research Institute’s Dennis Wolan takes you on a fascinating exploration of the human body’s ecosystem and the myriad symbiotic relations found there that sustain and affect everything from immunity to behavior, and how his lab “mines” this microbiome for potential therapies. Series: "Saturday Science at The Scripps Research Instititute" [4/2016] [Science] [Show ID: 30497]
Skoltech Colloquium: Numerical Algorithms for High-Dimensional Problems, 30.01.2014
January 30, 2014 Numerical Algorithms for High-Dimensional Problems Speaker: Ivan Oseledets, Associate Professor at the Skolkovo Institute of Science of Technology (Skoltech). He previously was a Senior Researcher in the Institute of Numerical Mathematics of Russian Academy of Sciences (INM RAS) in Moscow, Russia (till 2013, from 2013-part time). Ivan has received the PhD degree in Numerical Mathematics from the INM RAS in 2007 and the degree of Doctor of Sciences (second Russian degree) in 2012. Ivan was an invited Professor in the Haussdorf Institute of Mathematics in Bonn in 2011, and also has a part-time position in the Max-Planck Institute for Mathematics in the Sciences (Leipzig) from 2009. Dr. Oseledets research interests include numerical analysis, linear algebra, tensor methods and their applications in high-dimensional problems: solution of PDEs, quantum chemistry and computational material design, stochastic partial differential equations, wavelets, data mining and compression. Dr. Oseledets has received the medal of Russian academy of Sciences for the best student work in Mathematics in 2005; the medal of Russian academy of Sciences for the best work among young mathematicians in 2009. He is the winner of the Dynasty Foundation contest among young mathematicians in Russia in 2012. Abstract: Multidimensional problems are notoriously difficult due to the curse of dimensionality. However, high-dimensional problems are usually the most interesting ones and moreover, if the problem is of a considerable practical interest, often there is a method that solves it. The most vivid example is the Schrodinger equation in quantum chemistry, where efficient solution methods have been proposed. However, such methods are usually problem-specific, require a lot of efforts to implement and difficult to be applied in other areas. In the recent years, active development of mathematical foundations for the algorithms for the solution of high-dimensional problem has begun. Novel tensor formats (Hierarchical Tucker, Tensor Train) as well as surprinsing connections with other research areas (MPS, PEPS, tensor networks, graphical models) form a new research area with new fascinating theoretical and algorithmic problems and new applications in chemistry, biology and data-mining and global optimization.
Views: 869 Skoltech
Mining Engineering Career Opportunities Field Salary Colleges by BrainChecker
http://www.brainchecker.in Mining Engineering Career by BrainChecker Stay tuned for regular updates from BrainChecker Channel. We provide excellent education related tips and excellent career guidance. Contact: https://goo.gl/forms/cmB1rRC4v5qF2rf73 Fill the form above and we would get in touch with you Hey there and welcome to the Brainchecker's YouTube channel, India’s largest Career Counseling Company!! Our entire video would be divided into 5 sections: - Introduction. - Nature of work. - Eligibility and Professional Courses available. - Best Colleges - Career prospects and Salary Students are requested to perform their own due research before choosing a career. You can check the description for additional details and assistance from Brain Checker. Introduction Mining engineering is an engineering discipline that applies science and technology to the extraction of minerals from the earth. Mining engineering is associated with many other disciplines, such as geology, mineral processing and metallurgy, geotechnical engineering and surveying. A mining engineer may manage any phase of mining operations – from exploration and discovery of the mineral resource, through feasibility study, mine design, development of plans, production and operations to mine closure. Nature of Work Mining Engineers design mines and will use engineering principles, technology and scientific theory for the safe and effective extraction of natural resources from these mines. Mining Engineers plan, design and operate the mining processes, both underground and above ground. Mining Engineers will be responsible for the overseeing of both mining operations and miners, and are employed by many mining-related organizations Duties The duties of a mining engineer include: • Involved in the design of both open-pit and underground mines • Overseeing of the operations of the mine • Training and supervision of personnel • Looking into methods for transporting minerals to the various processing plants • Oversee the design and construction of mine shafts and tunnels in underground mines • To ensure that mines are operated safely and in the most environmentally effective ways possible • Oversee the production rates to assess the effectiveness of the mine operation • To be involved in finding solutions for problematic areas such as water and air pollution, land reclamation etc. • Design and develop mines and determine the best way to extract metal or minerals to get the most out of deposits, and to extract as much out of the mine whilst maintaining strict safety and environmental issues at hand, for the least amount of money. • Involved in trying to limit the amount of water used in the operation, and keep pollution to a bare minimum. • Prepare technical reports for miners, engineers, and managers Now let’s go to, Eligibility and Professional Courses • 10+2 Science with Physics, Chemistry, Mathematics is mandatory with at least 60% marks. • Entrance examinations are conducted for admission to B.E./B.Tech programs in marine engineering. Some colleges could consider the marks obtained in 10+2 qualifying examination as well, for selection into these programs. • M.E./M.Tech programs can be pursued if the individual has completed B.E./B.Tech in the same discipline. Moreover, they need to qualify for GATE as well. We at Brain Checker help students in choosing their career. To know if this career suits your talents of skillsets, you can consult a Brain Checker Career Specialist. Check link in the description for more details. Now we are going to look at few good colleges offering this qualification : 1 IIT Kharagpur, West Bengal 2 IIT - BHU, Uttar Pradesh 3 Indian School of Mines, Jharkhand 4 NIT Surathkal, Karnataka 5 Visvesvaraya National Institute of Technology Nagpur, Maharashtra Moving on to the next part of the video........ Career Prospects There are a lot of opportunities for Mining Engineering graduates in the following fields,. Arab countries like Saudi Arabia, Kuwait, Qatar and UK provides profitable career opportunities to the eligible candidates. Job Profiles include: • Mining Engineer – Granite • Mining Engineering Technicians • Research Engineers- Data Mining • Assistant Mining Engineer • Mining Engineer • Mine Planner • Technical Consultant amongst others An Mining Engineering graduate gets an average salary between Rs.40,000 and Rs 50,000 per month at the entry level. It also depends on the university you graduate from. Top universities will fetch their graduates higher salary packages. After a couple of years of experience an individual can earn up to Rs 3,00,000 per month or more depending on the skill set, experience and performance. Thank you for watching, if you loved this Brain Checker Video please like, share and subscribe to us. Bye!!
How to calculate Standard Deviation and Variance
Tutorial on calculating the standard deviation and variance for statistics class. The tutorial provides a step by step guide. Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos: How to Calculate Mean and Standard Deviation Using Excel http://www.youtube.com/watch?v=efdRmGqCYBk Why are degrees of freedom (n-1) used in Variance and Standard Deviation http://www.youtube.com/watch?v=92s7IVS6A34 Playlist of z scores http://www.youtube.com/course?list=EC6157D8E20C151497 David Longstreet Professor of the Universe Like us on: http://www.facebook.com/PartyMoreStudyLess Professor of the Universe: David Longstreet http://www.linkedin.com/in/davidlongstreet/ MyBookSucks.Com
Views: 1366450 statisticsfun
Techniques for random sampling and avoiding bias | Study design | AP Statistics | Khan Academy
Techniques for random sampling and avoiding bias. View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/gathering-data-ap/sampling-methods/v/techniques-for-random-sampling-and-avoiding-bias?utm_source=youtube&utm_medium=desc&utm_campaign=apstatistics AP Statistics on Khan Academy: Meet one of our writers for AP¨_ Statistics, Jeff. A former high school teacher for 10 years in Kalamazoo, Michigan, Jeff taught Algebra 1, Geometry, Algebra 2, Introductory Statistics, and AP¨_ Statistics. Today he's hard at work creating new exercises and articles for AP¨_ Statistics. Khan Academy is a nonprofit organization with the mission of providing a free, world-class education for anyone, anywhere. We offer quizzes, questions, instructional videos, and articles on a range of academic subjects, including math, biology, chemistry, physics, history, economics, finance, grammar, preschool learning, and more. We provide teachers with tools and data so they can help their students develop the skills, habits, and mindsets for success in school and beyond. Khan Academy has been translated into dozens of languages, and 15 million people around the globe learn on Khan Academy every month. As a 501(c)(3) nonprofit organization, we would love your help! Donate or volunteer today! Donate here: https://www.khanacademy.org/donate?utm_source=youtube&utm_medium=desc Volunteer here: https://www.khanacademy.org/contribute?utm_source=youtube&utm_medium=desc
Views: 81869 Khan Academy
Whats Eating Scientific Data?  21st Century Approaches to
Google Tech Talk June 17, 2009 ABSTRACT Whats eating Scientific Data? 21st Century Approaches to Discovering (Chemical) Data Presented by Jim Downing and Nico Adams. The web of documents and unstructured information is slowly but inexorably evolving towards a web of data. The increasing data-centricity of the web is driven by the next generation of web-applications and the future evolution of search - the searching of structured data is the value proposition behind a recent spate of start-ups in the search space. Furthermore, the internet in general and the semantic web in particular are revolutionising the way in which science communicates, manages and exchanges data, impacting all areas of scientific endeavour from scholarly communication through to laboratory management and data analysis and mining. Chemistry is the central physical science and at the heart of modern research into new drugs, new materials and new personal care products. All of these products require the confluence of structured data from a number of different domains and often advances in science can be viewed as a data integration problem and therefore the availability as well as the discoverability of high-quality scientific/chemical data on the internet is of the utmost importance. In this talk we will discuss recent developments in the semantic toolstack for chemistry, starting with markup languages for chemical data, RDF vocabularies as well as ontologies (ChemAxiom) for chemicals and materials (data). It will illustrate how ontologies can be used for indexing, faceted search and retrieval of chemical information and for the "axiomatisation" of chemical entities and materials beyond simple notions of chemical structure. We will discuss the use of linked data to generate new chemical insights and will provide a brief discussion of the use of our entity extraction and natural language processing system OSCAR for the "semantification" of chemical information. We will demonstrate the use of authoring tools (Chem4Word) for the generation of structured "datuments" (data + documents) on the web as well as the Lensfield data processing and publication system. There will also be a brief discussion on how some of the principles developed for chemistry can be applied to other domains, such as biomedical research. Finally, we will review some of the challenges that are facing both chemical data and the adoption of semantic web technologies today. Biosketch Nico Adams: Nico Adams read chemistry the University of York and subsequently worked as a research chemist at DSM Research (The Netherlands) and Cambridge Combinatorial (now Millenium Pharmaceuticals, UK), on the combinatorial synthesis and screening of early transition metal olefin polymerisation catalysts. He subsequently became a member of the group of Prof P. Mountford at the Inorganic Chemistry Laboratory, University of Oxford to read towards his doctoral degree in organometallic chemistry. In 2003 he joined the Technische Universiteit Eindhoven as a post-doctoral research associate (group of Prof U. S. Schubert) and the Dutch Polymer Institute (DPI) as a project leader in polymer informatics. In 2006 he joined the University of Cambridge as a research associate, where he manages a research group in polymer informatics. His main research interests lie in the area of combinatorial and solid phase organometallic chemistry, materials and polymer informatics, the use of polymers for biomedical applications as well as ontological engineering and the semantic web. Biosketch Jim Downing: After completing a Masters in computational fluids and mechanics, Jim spent 4 years with a small software start-up in Cambridge working on information and knowledge systems in science and engineering research, and later in public sector information. He moved to the University of Cambridge in 2004 to work on the Open Source DSpace institutional repository software. Working with early adopters of the DSpace system at Cambridge (particularly Prof. Peter Murray-Rust) led to an interest in chemical information, and to Jim joining Prof. Murray-Rust's group to develop software architectures for chemical information, including a move towards semantic web technologies and RESTful web APIs. Jim is currently interested in the application of Linked Data in chemistry and the opportunities and challenges presented by functional programming languages in cheminformatics.
Views: 4170 GoogleTechTalks
Data Science @Stanford- Bonnie Berger, PhD
Bonnie Berger, PhD, head of the Computational and Biology group at MIT's Computer Science and Artificial Intelligence Laboratory will discuss applying mathematical techniques to problems in molecular biology.
Views: 6294 Stanford
Statistics - How to find outliers
This video covers how to find outliers in your data. Remember that an outlier is an extremely high, or extremely low value. We determine extreme by being 1.5 times the interquartile range above Q3 or below Q1. For more videos visit http://www.mysecretmathtutor.com
Views: 397383 MySecretMathTutor
Some Statistical Problems in Spectroscopy and Hyperspectral Imaging
Google Tech Talks July 23, 2008 ABSTRACT Every material has a distinctive spectrum. The spectrum of a material tells us about its chemistry. Hyperspectral images produce a spectrum (represented as several hundred numbers) at each pixel in an image. So hyperspectral images enable us to map variations in chemistry. The first hyperspectral scanners, built in the 1980's and 1990's, were designed for airborne applications, primarily for mineral, environmental and military applications. However, in recent years, hyperspectral microscopes and cameras have been developed and are being used for terrestrial applications in areas such as medical diagnosis, burns analysis and skin cancer, biosecurity, pharmaceuticals, forensics and in agribusiness. A significant issue in hyperspectral imaging is that the spectra at many pixels in an image are actually mixtures of the spectra of the pure ingredients. My main focus over a number of years has been on developing fast and sophisticated algorithms and software for "unmixing" these spectra into their pure ingredients, both when the pure ingredients are known and when they are unknown. This has resulted in two software packages: The Spectral Assistant (TSA), which has been incorporated into another CSIRO package, The Spectral Geologist, which itself has been sold to over 100 (mainly exploration and mining) companies around the world; and Iterated Constrained Endmembers (ICE), which has yet to be commercialized. I will give an overview of the algorithms underlying TSA and ICE, and demonstrate their application to some mineral, remotely sensing and biological data sets. Finally, I will discuss some unsolved statistical and computational problems associated with these packages. Speaker: Mark Berman Mark Berman received the B.Sc.(Hons.) degree and University Medal in mathematical statistics from the University of New South Wales in 1974, and the Master of Statistics degree from the same institution in 1976. In 1978, he was awarded the Ph.D. and D.I.C. degrees in mathematical statistics by the Imperial College of Science and Technology, London. He was a visiting lecturer in the Department of Statistics at the University of California, Berkeley during 1978-1979. Most of his time since then has been with the CSIRO Division of Mathematical and Information Sciences (CMIS), Sydney, where he is now a Chief Research Scientist. He led CMIS' Image Analysis Group from 1989 to 2000. He spent 1988 at the Melbourne Research Laboratories of Broken Hill Proprietary Ltd. where he established the Image Processing and Data Analysis Group. His research interests are in image analysis (especially hyperspectral), spectroscopy and spatial data analysis. Since 2007, Dr. Berman has been working part time at CMIS. During this period, he has also given Ph.D courses in spectroscopy and hyperspectral image analysis at the Technical University of Denmark and Stanford University.
Views: 17183 GoogleTechTalks
Range, variance and standard deviation as measures of dispersion | Khan Academy
Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/e/variance?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/v/variance-of-a-population?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/box-and-whisker-plots/v/range-and-mid-range?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1188758 Khan Academy
Sampling Techniques [Hindi]
The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. The key benefit of probability sampling methods is that they guarantee that the sample chosen is representative of the population
Views: 101920 Manager Sahab
How To... Calculate Pearson's Correlation Coefficient (r) by Hand
Step-by-step instructions for calculating the correlation coefficient (r) for sample data, to determine in there is a relationship between two variables.
Views: 376401 Eugene O'Loughlin
Crowdsourced Chemistry  Why Online Chemistry Data Needs Your Help
This is the Ignite talk that I gave at ScienceOnline2010 #sci010 in the Research Triangle Park in North Carolina on January 16th 2010. This was supposed to be a 5 minute talk highlighting the quality of chemistry data on the internet. Ok, it was a little tongue in cheek because it was an after dinner talk and late at night but the data are real, the problem is real and the need for data curation of chemistry data online is real. On ChemSpider we have provided a platform to deposit and curate data. Other videos will show that in the future.
Views: 290 Antony Williams
Scientific Studies: Last Week Tonight with John Oliver (HBO)
John Oliver discusses how and why media outlets so often report untrue or incomplete information as science. Connect with Last Week Tonight online... Subscribe to the Last Week Tonight YouTube channel for more almost news as it almost happens: www.youtube.com/user/LastWeekTonight Find Last Week Tonight on Facebook like your mom would: http://Facebook.com/LastWeekTonight Follow us on Twitter for news about jokes and jokes about news: http://Twitter.com/LastWeekTonight Visit our official site for all that other stuff at once: http://www.hbo.com/lastweektonight
Views: 13744014 LastWeekTonight
ROC Curves and Area Under the Curve (AUC) Explained
An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 256530 Data School
Mean; Median; Mode; Standard Deviation
This clip show the calculation of each of these values for a small data set.
Views: 483480 John Quinn
How To... Perform a Chi-Square Test (By Hand)
Also known as a "Goodness of Fit" test, use this single sample Chi-Square test to determine if there is a significant difference between Observed and Expected values. This video shows a step-by-step method for calculating Chi-square.
Views: 335426 Eugene O'Loughlin
Lecture 74 — How to Construct a Tree | Stanford University
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Fellow Short Talks: Dr Scott Hale, Oxford University
Bio Dr Scott A. Hale is a Faculty Fellow with expertise in both the social sciences and computer science. His research focuses on knowledge discovery, data mining, and the visualization of human behaviour in three substantive areas: multilingualism and user experience, mobilization/collective action, and human mobility. Research New technologies generate unprecedented quantities of digital trace data about human behaviour and provide opportunities to study complex social systems in frameworks similar to those of the natural sciences. His work concentrates on empirical observation of patterns in large-scale data and experiments. These approaches form part of a new field — computational social science or social data science — and can generate theory-informed predictive models and change the way we understand and solve social problems. The vast majority of the data about human behaviour, however, is unstructured, and my research therefore seeks to develop new tools to take full advantage of these data.
The Center for Research Computing at Notre Dame
The Center for Research Computing (CRC) is Notre Dame’s innovative and multidisciplinary computational research unit. It supports discoveries in all disciplines through advanced computation, data analysis, and other digital research tools. The CRC houses three complementary resources: the High Performance Computing section provides more than 20,000 cores of computational power with support infrastructure for both hardware and installed software; Cyberinfrastructure Development empowering all CRC partners to develop research environments that support advanced data and information processing services, including acquisition, fusion, storage, management, integration, mining, and data visualization; and Research Software development. At the CRC, Notre Dame is advancing scientific discovery through computation. Learn more at https://crc.nd.edu/
Understanding Confidence Intervals: Statistics Help
This short video gives an explanation of the concept of confidence intervals, with helpful diagrams and examples. Find out more on Statistics Learning Centre: http://statslc.com or to see more of our videos: https://wp.me/p24HeL-u6
Views: 678591 Dr Nic's Maths and Stats
CAREERS IN ZOOLOGY – B.Sc,M.Sc,Zoologists,Job Openings, Research,Salary package
CAREERS IN ZOOLOGY.Go through the career opportunities of ZOOLOGY, Govt jobs and Employment News channel from Freshersworld.com – The No.1 job portal for freshers in India. Visit http://www.freshersworld.com for detailed Career information,Job openings,Education details of ZOOLOGY. There’s no doubt one of the upcoming careers in India and the prospect of great job opportunities makes it worthy to seek a career in this field Zoology is a branch of biology which is involved in scientific study of animals, including protozoa, fish, reptiles, mammals and birds. In other words, Zoology is a branch of science which deals with study of animals and their existence in the environment. Zoology comprises topics for study, like structure & anatomy, characteristics, behavioural pattern, nutrition, distribution, physiology, genetics, evolution, classification etc. of zoological species (animals). Zoology includes study of household pets, marine animals, zoo animals, wild animals etc. The professionals who study this field are called as Zoologists. Since Zoology is a branch of biology related to animal kingdom, Zoologists are also called as animal biologists or animal scientists. The branch of zoology specialising in study of birds is called as ornithology, specialising in study of fish and their habitats is known as Icthylogy or Fisheries. The branch of Zoology which studies amphibians and reptiles is called as Herpetology and which is involved in studying mammals, is called as Mammalogy. An aspirant of Zoology can get a job either as an academician or on project basis in research. A Zoologist not only works in a zoo but in a natural habitat too. His job includes preparing various reports on traits and behavioural aspects of animals and managing them. A zoologist works in the field as well as in a laboratory. Other jobs include zookeeper, animal caretaker, veterinary technician/technologist, research assistant, laboratory technician etc. A school teaching job for science subject along with other essential qualifications like B.Ed or D. Ed could be an option too. Educational qualification in Zoology includes Bachelor in Science (B.Sc.), Master in Science (M. Sc.) and Doctorate degree (Ph.D). Most of the universities and colleges in India of various states having courses in fundamental biology, provide courses in Zoology too. Foreign universities also provide similar degree of education in zoology. In case of job, it is always good to have higher level education for better understanding, greater responsibility and more salary. For those who want to make their career in research, a master’s degree or doctorate degree is must. For any job in zoology, apart from educational qualifications, other skills like critical thinking, problem solving attitude, ability to deal in bad weathers, active learning, quick decision making, etc are also useful. Computer skills like data analysis, knowledge of MS office particularly MS Excel (for data mining), MS word for report making is must. Last but not the least, love for animals and curiosity for science is very essential for those who want to make their career in Zoology. In Industry, Zoologists can be hired by companies working closely with animals like manufacturing veterinary products. Salary depends upon various factors like educational qualification, location of job, form of job etc. However, on an average a zoologist should earn not less than 3.5 lakhs per year. For more jobs & career information and daily job alerts, subscribe to our channel and support us. You can also install our Mobile app for govt jobs for getting regular notifications on your mobile. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com - - ***Disclaimer: This is just a career guidance video for fresher candidates. The name, logo and properties mentioned in the video are proprietary property of the respective companies. The career and job information mentioned are an indicative generalised information. In no way Freshersworld.com, indulges into direct or indirect recruitment process of the respective companies.
Statistics intro: Mean, median, and mode | Data and statistics | 6th grade | Khan Academy
This is a fantastic intro to the basics of statistics. Our focus here is to help you understand the core concepts of arithmetic mean, median, and mode. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/e/calculating-the-mean?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/v/mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/histograms/v/interpreting-histograms?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1847809 Khan Academy
Jure Leskovec, "The Web as a Laboratory for Studying Humanity", by  Jure Leskovec 2011-10-24:
Speaker: Jure Leskovec Event Details http://www.sfbayacm.org/event/dmsig-1024-jure-leskovec-web-laboratory-studying-humanity With an increasing amount of social interaction taking place in on-line settings, we are accumulating massive amounts of data about phenomena that were once essentially invisible to us: the collective behavior and social interactions of hundreds of millions of people. Analyzing this massive data computationally offers enormous potential both to address long-standing scientific questions, and also to harness and inform the design of future social computing applications: What are emerging ideas and trends? How is information being created, how it flows and mutates as it is passed from a node to node like an epidemic? How will a community or a social network evolve in the future? We discuss how computational perspective can be applied to questions involving structure of online networks and the dynamics of information flows through such networks, including analysis of massive data as well as mathematical models that seek to abstract some of the underlying phenomena. Speaker Bio Jure Leskovec (http://cs.stanford.edu/~jure) is an assistant professor of Computer Science at Stanford University where he is a member of the Info Lab and the AI Lab. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems he investigates are motivated by large scale data, the Web and on-line media. He received six best paper awards, a ACM KDD dissertation award, Microsoft Research Faculty Fellowship and appeared on IEEE Intelligent Systems magazine "AI's 10 to Watch". Jure also holds three patents. Before joining Stanford Jure spent a year as a postdoctoral researcher at Cornell University. He completed his Ph.D. in computer science at Carnegie Mellon University in 2008. Jure has authored the Stanford Network Analysis Platform (SNAP), a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes, and billions of edges.
Views: 1527 San Francisco Bay ACM
Tio2 China Market Research and Import and Export Analysis Platforms
CCM Chemicals provides information data and analytics for Chinese chemical Industry and markets. You can benefit from Chemical Industry Reports, Monthly newsletters on different chemical Products and Market data. We provide a clear picture of Chinese Chemical markets, Import and Export Analysis and Trade information. Reports and newsletters: http://onlineplatform.cnchemicals.com/en/home/op Trade Data and Import Export Analysis: http://tranalysis.com/Html/index.html
StatsCast: What is a t-test?
For more, visit http://www.statscast.net This video explains the purpose of t-tests, how they work, and how to interpret the results.
Views: 654461 StatsCast
Artificial Neural Networks Explained !
Contact me on : [email protected] Neural Networks is one of the most interesting topics in the Machine Learning community. Their potential is being recognized every day as the technology is advancing at an ever growing rate. From being a topic of research for decades to practical use by thousands of organizations, Neural Networks have come a long way. Today there are a number of jobs available in Machine Learning from application to research domain. But Machine Learning is not like conventional programming. It requires a different line of thinking than what conventional programming has taught us.  This might become a problem for people interested in learning Machine Learning. A lot of mathematical concepts are deeply embedded in ML and an understanding of these core concepts will help anyone starting with ML go long way ahead. Trust me! thats the only way. In this video I have tried to make those core concepts a little bit clearer by using a real-life example. This video is about how simply you can understand the working of an Artificial Neural Network. There are a lot of questions which can come to your mind after watching this video, but do not focus on the "WHY" as much as on the "HOW" of what has been explained. A detailed explanation of each of the mentioned terms will be covered in the future videos.
Views: 42614 Harsh Gaikwad
Dr. Michel Dumontier from Stanford University presents a lecture on "Ontologies." Lecture Description Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery. View slides from this lecture: https://drive.google.com/open?id=0B4IAKVDZz_JUVjZuRVpMVDMwR0E About the Speaker Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
Computational Analysis and Integration of Large-Scale Biological Data with Deep Learning Approaches
Presenter: Tunca Dogan KanSiL, Department of Health Informatics, Graduate School of Informatics, ODTU European Molecular Biology Laboratory, European Bioinformatics Institute * This version doesn't have the annotations made by the presenter. To watch the original version, you can register for free and watch it here: https://www.bigmarker.com/bioinfonet/TuncaDogan Abstract: Machine learning and data mining techniques are frequently employed to make sense of large-scale and noisy biological/biomedical data accumulated in public servers. A key subject in this endeavour is the prediction of the properties of proteins such as their functions and interactions. Recently, deep learning (DL) based methods have outperformed the conventional machine learning algorithms in the fields of computer vision, natural language processing and artificial intelligence; which brought attention to their application to the biological data. In this talk, I'm going to explain the DL-based probabilistic computational methods we have recently developed in our research center (KanSiL, Graduate School of Informatics, ODTU); first, to predict the functions of the uncharacterised proteins (i.e., DEEPred); and second, to identify novel interacting drug candidate molecules for all potential targets in the human proteome (i.e., DEEPscreen) to serve the purposes of drug discovery and repositioning, together with the aim of biomedical data integration. Apart from the benefits of employing novel DL approaches, I'll also mention the limitations of DL-based techniques when applied on the biological data, to explain why deep learning alone cannot solve every problem related to bioinformatics.
Views: 148 RSG-Turkey
Publishing Your Research 101 - Episode 9 p1: The Basics of Copyright
This episode looks at some of the issues around copyright and fair use of information. Most scientists encounter these issues at three points: when they are submitting a manuscript for publication, when they want to use items from their published articles in other publications, presentations, or in teaching, and when they want to use items published by other scientists in their own publications, presentations, or in teaching. Beyond that, as citizens in a digital world, we run into some of the same questions when using any content, scientific or not, on the Web. The first part of this episode looks at what copyright is, what happens when you transfer copyright to a publisher, and what rights you as an author retain when you transfer copyright. The second part discusses fair use, which are exceptions to copyright when certain conditions apply. Subscribe! http://bit.ly/AmerChemSOc Facebook! https://www.facebook.com/ACSPublications/ Twitter! https://pubs.acs.org/page/follow.html?widget=follow-pane-twitter For more information, please visit the ACS Publications website: https://pubs.acs.org/ You might also like: ACS PUBS – Website Demonstration and Tutorials: https://www.youtube.com/watch?v=Zltq_uhyeyA&list=PLLG7h7fPoH8IgaVECo9hdA-LzgvLk0obW ACS Energy Letters Perspectives & Reviews: https://www.youtube.com/watch?v=TVrXLFpoQg4&list=PLLG7h7fPoH8LweV1etr5ckSxnfLU-KkoM ACS Pubs – Fresh Faces of ACS: https://www.youtube.com/watch?v=sxuH-wbibqo&list=PLLG7h7fPoH8JsjoWywWXjoOdwJkBt4hcY Produced by the American Chemical Society, the world’s largest scientific society. ACS is a global leader in providing access to chemistry-related information and research through its multiple databases, peer-reviewed journals and scientific conferences. Join the American Chemical Society! https://bit.ly/Join_ACS
find relevant notes-https://viden.io/search/knowledge?query=computer+science also search PDFs notes-https://viden.io More videos like this: https://www.youtube.com/playlist?list=PL9P1J9q3_9fNmTX2ZkUnboMBp8yU_GHYj
Views: 77361 LearnEveryone
Panelist: Numenta
NumentaThe Numenta Platform for Intelligent Computing is now available. The first release of the Numenta Platform for Intelligent Computing (NuPIC) is a research release targeted at sophisticated developers for the purpose of education and experimentation. NuPIC implements a hierarchical temporal memory system (HTM) patterned after the human neocortex. We expect NuPIC to be used on problems that, generally speaking, involve identifying patterns in complex data. The ultimate applications likely will include vision systems, robotics, data mining and analysis, and failure analysis and prediction. Numenta is committed to creating and supporting an open, collaborative community of companies and individuals interested in working on HTM systems. Concurrent with the Numenta Platform release, Numenta also has launched developer community tools and training materials. For an overview of HTM, see Technology. For more information on Numenta, see About Numenta. For more detailed information on HTM and NuPIC, see Education. To download the research release, see Software. http://www.numenta.com/
Views: 3282 CITRIS
Introduction to Data Science Using R & KNIME
Research Stash is excited to bring you the first data science workshop on Introduction to Data Science Using R & KNIME. This workshop will be broadcasted LIVE on Research Stash. The workshop will be given by Dr. Kasthuri Kannan, He is the Assistant Professor of Pathology, Genome Technology Center (GTC) Core Labs, NYU, USA. Dr. Kannan’s research interests are in genomics and computational biology. The rapid advancement of genomics technologies has enabled unbiased data-driven molecular approaches in medicine (as opposed to hypothesis-based studies). Accordingly, he is interested in data-driven genomic analysis that leads to verifiable and useful hypotheses. His contributions to genomics include a comprehensive report of molecular alterations in adenoid cystic carcinoma and high-frequency ATRX mutations in astrocytoma. Thanks for being patient, here is the video recording of the workshop. We really appreciate your enthusiasm to be a part of this first online workshop. If you have any suggestions/feedback ~ Feel free to write us at [email protected]
Views: 399 Research Stash
Exploiting Data Mining for Authenticity Assessment and Protection of .. (KDD 2015)
Exploiting Data Mining for Authenticity Assessment and Protection of High-Quality Italian Wines from Piedmont KDD 2015 Marco Arlorio Jean Daniel Coisson Giorgio Leonardi Monica Locatelli Luigi Portinale This paper discusses the data mining approach followed in a project called TRAQUASwine, aimed at the definition of methods for data analytical assessment of the authenticity and protection, against fake versions, of some of the highest value Nebbiolo-based wines from Piedmont region in Italy. This is a big issue in the wine market, where commercial frauds related to such a kind of products are estimated to be worth millions of Euros. The objective is twofold: to show that the problem can be addressed without expensive and hyper-specialized wine analyses, and to demonstrate the actual usefulness of classification algorithms for data mining on the resulting chemical profiles. Following Wagstaff's proposal for practical exploitation of machine learning (and data mining) approaches, we describe how data have been collected and prepared for the production of different datasets, how suitable classification models have been identified and how the interpretation of the results suggests the emergence of an active role of classification techniques, based on standard chemical profiling, for the assesment of the authenticity of the wines target of the study.
Cogistry: Accelerating Algorithmic Chemistry via Cognitive Synergy - Ben Goertzel
Speculative ideas for new AI/AGI/Artificial-Life/Artificial-Chemistry development that could be done on top of the OpenCog platform, leveraging OpenCog tools in a novel way. These are not necessarily proposed for immediate-term development – we have a lot of other stuff going on, and a lot of stuff that’s half-finished or ¾-finished or more, that needs to be wrapped up and improved, etc. They are, however, proposed as potentially fundamental enhancements to the OpenCog and PrimeAGI (CogPrime) designs in their current form. The core motivation here is to add more self-organizing creativity to the AGI design. One can view these ideas as extending what MOSES does in the PrimeAGI design – MOSES (at least in its aspirational version) is artificial evolution enhanced by pattern mining and probabilistic reasoning; whereas Cogistry is, very loosely, more like artificial biochemistry similarly enhanced. http://blog.opencog.org/2016/10/25/cogistry-accelerating-algorithmic-chemistry-via-cognitive-synergy/ Algorithmic Chemistry: A Model for Functional Self-Organization - Walter Fontana https://www.santafe.edu/research/results/working-papers/algorithmic-chemistry-a-model-for-functional-self- Replicode: A constructivist programming paradigm and language http://wiki.humanobs.org/_media/public:publications:replicode.v2.4.rutr-scs13002.pdf This talk was part of the Hong Cogathon that was held at Robotics Garage Dec 2016 http://wiki.opencog.org/w/Hong_Cogathon_Dec_2016 Many thanks for watching! Consider supporting me by: a) Subscribing to my YouTube channel: http://youtube.com/subscription_center?add_user=TheRationalFuture b) Donating via Patreon: https://www.patreon.com/scifuture and/or c) Sharing the media I create Kind regards, Adam Ford - Science, Technology & the Future: http://scifuture.org
Warframe - Understanding Saryn (Poisoning Chemistry)
This Video is OUTDATED! The NEW SARYN VIDEO LINK IS HERE: https://www.youtube.com/watch?v=Ket7zwvmyPs Hey Tennos~ So took me 5 days to finish this video =.=* a lot of research and fooling around with saryn and all of saryn's abilities. I hope all you warframe fanatics find this useful especially if you are new and really like saryn. Please let me know any important stuff about her that I missed or even if I got wrong at the comments below. Also let me know if you learned something new from watching this Saryn Guide video :3 Apparently my thumbnails are really bad and not clickbait worthy so for this video someone else volunteered to do my thumbnail, it would mean a lot to me if you could give me some feedback if it is better! Thumbnail Artist : FaceMcMegami from Discord Title Suggester : Anubis from Discord Saryn's AOE Instant Kill Bug Video: https://www.youtube.com/watch?v=WFaVQ1_jCKc&list=PL5vuR3PSGaiE7JtbmhKwrQBARTSTpJ2w8&index=5 Joey Zero's Video on Saryn Build: https://www.youtube.com/watch?v=Bz0ut0m_6Us Distant Observer Saryn + Torid Video: https://www.youtube.com/watch?v=SWTTpuVjrgA Blast + Stealth Damage Video: https://www.youtube.com/watch?v=ROwa_-gy3Rg&list=PL5vuR3PSGaiGJtK_wc7KYuDrPb_uHRBfC&index=31 Warframe (PC) IGN : Cyaneous Steam : x3lp Discord : https://discord.gg/YSh6Jh3 Twitter : https://twitter.com/x3lplive Twitch : http://www.twitch.tv/x3lplive Vid.me : https://vid.me/x3lplive
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