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^ Read * Statistical Bioinformatics: For Biomedical and Life Science Researchers by Jae K. Lee ✓ eBook or Kindle ePUB. Statistical Bioinformatics: For Biomedical and Life Science Researchers "A Disappointment" according to statprofA Disappointment We evaluated this book as a textbook for a graduate level statistics class. It surveys a large number of concepts in techniques, including the requisite chapter on concepts in probability and distributions--each chapter is written by a different author. Other topics include quality control, supervised classification, unsupervised analysis, experimental designs, network analysis, and techniques for GWAS studies. Overall, the depth is extrem
| Title | : | Statistical Bioinformatics: For Biomedical and Life Science Researchers |
| Author | : | |
| Rating | : | 4.49 (612 Votes) |
| Asin | : | 0471692727 |
| Format Type | : | paperback |
| Number of Pages | : | 384 Pages |
| Publish Date | : | 2013-06-21 |
| Language | : | English |
. He was previously a research scientist in the Laboratory of Molecular Pharmacology, National Cancer Institute. Jae K. He earned his doctorate in statistical genetics from the University of Wisconsin, Madison. Lee, Ph.D., is a professor of biostatistics and epidemiology in the Department of Health Evaluation Sciences at the University of Virginia School of Medicine, where he designed and teaches a course on Stati
It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.. This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statisticsEnables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferencesEnables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysisCarefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysisOffers programming examples and datasetsIncludes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical materialFeatures supplementary materials, including datasets, links, and a statistical package available onlineStatistical Bioinformat
"A Disappointment" according to statprofA Disappointment We evaluated this book as a textbook for a graduate level statistics class. It surveys a large number of concepts in techniques, including the requisite chapter on concepts in probability and distributions--each chapter is written by a different author. Other topics include quality control, supervised classification, unsupervised analysis, experimental designs, network analysis, and techniques for GWAS studies. Overall, the depth is extremely shallow for most topics and includes just a few equations or concepts. Although it does include snippets of R code, the included code is mostly one-liners without much in the way of ex. . We evaluated this book as a textbook for a graduate level statistics class. It surveys a large number of concepts in techniques, including the requisite chapter on concepts in probability and distributions--each chapter is written by a different author. Other topics include quality control, supervised classification, unsupervised analysis, experimental designs, network analysis, and techniques for GWAS studies. Overall, the depth is extremely shallow for most topics and includes just a few equations or concepts. Although it does include snippets of R code, the included code is mostly one-liners without much in the way of ex
Jae Lee and the authors present both basic and advanced topics, focusing on those that are relevant to the efficient and rigorous analysis of large data sets in biology.The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multidimensional visualization, experimental design, statistical resampling, and statistical network analysis. Dr. From the Back CoverA practical introduction to the underlying statistical concepts and techniques for successful use of bioinformatics toolsEffective use of the tools and methods of bioinfor
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