Fr. 130.00

Foundations for High-Throughput Omics Data Analysis - Methods, Theories and Applications

Anglais · Livre Relié

Expédition généralement dans un délai de 3 à 5 semaines

Description

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The aim of this textbook is to train new researchers in analyzing high-throughput omics data by building fundamental skills instead of focusing on technology or platform-specific features that change every few years. The authors seek a balance between breadth and depth in the broad field. The book contains many real examples to illustrate the methodological concept and biological relevance. Computer lab materials (data and hands-on programming code) are included along with homework exercises to provide real-world data analysis experiences.


Table des matières

    High-Throughput Omics Data. Experimental Design and Data Preprocessing. Differential and Association Analysis. Dimension Reduction. Robust Nonparametric Methods. Unsupervised Machine Learning and Clustering. Supervised Machine Learning I: Methods. Supervised Machine Learning II: Concept and Principles. Regularization Method. Bayesian Methods and Applications. Network Analysis. Enrichment Analysis. Meta-Analysis and Integrative Analysis. Selected Computational Algorithms. Reproducible Research and Critical Thinking in Bioinformatics. Appendix: Case Studies.

A propos de l'auteur

George C. Tseng is an associate professor in the Department of Biostatistics at the University of Pittsburgh, Pennsylvania, USA

Zhiguang Huo and Tianzhu Ma are PhD students at the University of Pittsburgh, Pennsylvania, USA

Résumé

The aim of this textbook is to train new researchers in analyzing high-throughput omics data by building fundamental skills instead of focusing on technology or platform-specific features that change every few years. The authors seek a balance between breadth and depth in the broad field. The book contains many real examples to illustrate the methodological concept and biological relevance. Computer lab materials (data and hands-on programming code) are included along with homework exercises to provide real-world data analysis experiences.

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