naveenbioinformatics,bioinformatics e-Books

About This Book

This book is designed to teach you bioinformatics software development. There is no computational biology here: no statistics, formulas, equations—not even explanations of the algorithms that underlie commonly used informatics software. The book’s examples are all based on the kind of data life science researchers work with and what they do with it. The book focuses on practical data management and manipulation tasks. The term “data” has a wide scope here, including not only the contents of databases but also the contents of text files, web pages, and other information sources. Examples focus on genomics, an area that, relative to others, is more mature and easier to introduce to people new to the scientific content of bioinformatics, as well as dealing with data that is more amenable to representation and manipulation in software. Also, and not incidentally,it is the part of bioinformatics with which the author is most familiar.

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The SingularityU The Netherlands Summit is aimed at bringing awareness about exponential technologies and their impact on business and policy.

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naveenbioinformatics,bioinformatics
Spencer Hall is a fourth year Statistics major and Philosophy minor. Hall plans on attending graduate school to pursue a master’s in Statistics with a concentration in Bioinformatics. Hall has had first-hand experience with a chronic illness that could be benefited by the application of bioinformatics. His talk is about how the massive amount of genetic data available today is too large to be analyzed without statistical methods, and has consequences so important that we cannot afford not to study it.
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naveenbioinformatics,bioinformatics
“Big data” is a catchall term for any data set of exceedingly large volume. It could be transaction information at a credit card company, invoice data at an online retailer, meteorological measurements from a weather station. All these data sets have unique characteristics that make it extremely difficult to use conventional computing technologies and techniques to store and process them for analysis. Their variety is daunting, and high velocity is required to handle them in a timely manner.

  •  People working in fields such as physics, bioinformatics, statistics, political science and psychology are already heavy users and analyzers of a large amount of data. Transition from these types of disciplines to big data analytics could be relatively smooth.
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