发帖
雷达卡
Editors: Ka-Chun Wong
Treats both theoretical and practical aspects of scalable data analysis in genome research
Covers various applications in high impact problems, such as cancer genome analytics
Includes concrete cases that illustrate how to develop solid computational pipelines for genomics
This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.
Table of contents
Front Matter
Pages i-viii
Statistical Analytics
Front Matter
Pages 1-1
Introduction to Statistical Methods for Integrative Data Analysis in Genome-Wide Association Studies
Pages 3-23
Robust Methods for Expression Quantitative Trait Loci Mapping
Pages 25-88
Causal Inference and Structure Learning of Genotype–Phenotype Networks Using Genetic Variation
Pages 89-143
Genomic Applications of the Neyman–Pearson Classification Paradigm
Pages 145-167
Computational Analytics
Front Matter
Pages 169-169
Improving Re-annotation of Annotated Eukaryotic Genomes
Pages 171-195
State-of-the-Art in Smith–Waterman Protein Database Search on HPC Platforms
Pages 197-223
A Survey of Computational Methods for Protein Function Prediction
Pages 225-298
Genome-Wide Mapping of Nucleosome Position and Histone Code Polymorphisms in Yeast
Pages 299-313
Cancer Analytics
Front Matter
Pages 315-315
Perspectives of Machine Learning Techniques in Big Data Mining of Cancer
Pages 317-336
Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms
Pages 337-355
NGS Analysis of Somatic Mutations in Cancer Genomes
Pages 357-372
OncoMiner: A Pipeline for Bioinformatics Analysis of Exonic Sequence Variants in Cancer
Pages 373-396
A Bioinformatics Approach for Understanding Genotype–Phenotype Correlation in Breast Cancer
Pages 397-428
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