Statistical Modeling and Machine Learning for Molecular Biology

This book PDF is perfect for those who love Mathematics genre, written by Alan Moses and published by CRC Press which was released on 06 January 2017 with total hardcover pages 255. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Statistical Modeling and Machine Learning for Molecular Biology books below.

Statistical Modeling and Machine Learning for Molecular Biology
Statistical Modeling and Machine Learning for Molecular Biology
Author : Alan Moses
File Size : 53,6 Mb
Publisher : CRC Press
Language : English
Release Date : 06 January 2017
ISBN : 9781482258622
Pages : 255 pages
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Statistical Modeling and Machine Learning for Molecular Biology by Alan Moses Book PDF Summary

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

Statistical Modeling and Machine Learning for Molecular Biology by Alan Moses

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

Get Book
Statistical Modeling and Machine Learning for Molecular Biology

Statistical Modeling and Machine Learning for Molecular Biology by Alan Moses

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

Get Book
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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications by K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar

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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 (

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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques Tools and Applications

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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

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