Statistical Modeling And Machine Learning For Molecular Biology
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Statistical Modeling and Machine Learning for Molecular Biology
Author | : Alan Moses |
Publsiher | : CRC Press |
Total Pages | : 255 |
Release | : 2017-01-06 |
Genre | : Mathematics |
ISBN | : 9781482258622 |
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Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.
Statistical Modeling and Machine Learning for Molecular Biology
Author | : Alan Moses |
Publsiher | : CRC Press |
Total Pages | : 264 |
Release | : 2017-01-06 |
Genre | : Mathematics |
ISBN | : 9781482258608 |
Download Statistical Modeling and Machine Learning for Molecular Biology Book in PDF, Epub and Kindle
Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.
Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications
Author | : K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar |
Publsiher | : Springer Nature |
Total Pages | : 318 |
Release | : 2020-01-30 |
Genre | : Technology & Engineering |
ISBN | : 9789811524455 |
Download Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications Book in PDF, Epub and Kindle
This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.
Gene Expression Data Analysis
Author | : Pankaj Barah,Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita |
Publsiher | : CRC Press |
Total Pages | : 276 |
Release | : 2021-11-08 |
Genre | : Computers |
ISBN | : 9781000425758 |
Download Gene Expression Data Analysis Book in PDF, Epub and Kindle
Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences
Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications
Author | : K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar |
Publsiher | : Unknown |
Total Pages | : 318 |
Release | : 2020 |
Genre | : Bioinformatics |
ISBN | : 9811524467 |
Download Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications Book in PDF, Epub and Kindle
This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.
Statistical Modelling of Molecular Descriptors in QSAR QSPR
Author | : Matthias Dehmer,Kurt Varmuza,Danail Bonchev |
Publsiher | : John Wiley & Sons |
Total Pages | : 437 |
Release | : 2012-09-13 |
Genre | : Medical |
ISBN | : 9783527645015 |
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This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR. The high-profile international author and editor team ensures excellent coverage of the topic, making this a must-have for everyone working in chemoinformatics and structure-oriented drug design.
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
Author | : Robert Gentleman,Vincent Carey,Wolfgang Huber,Rafael Irizarry,Sandrine Dudoit |
Publsiher | : Springer Science & Business Media |
Total Pages | : 478 |
Release | : 2005-12-29 |
Genre | : Computers |
ISBN | : 9780387293622 |
Download Bioinformatics and Computational Biology Solutions Using R and Bioconductor Book in PDF, Epub and Kindle
Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
Bioconductor Case Studies
Author | : Florian Hahne,Wolfgang Huber,Robert Gentleman,Seth Falcon |
Publsiher | : Springer Science & Business Media |
Total Pages | : 284 |
Release | : 2010-06-09 |
Genre | : Science |
ISBN | : 9780387772400 |
Download Bioconductor Case Studies Book in PDF, Epub and Kindle
Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: (1) import and preprocessing of data from various sources; (2) statistical modeling of differential gene expression; (3) biological metadata; (4) application of graphs and graph rendering; (5) machine learning for clustering and classification problems; (6) gene set enrichment analysis. Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.