Big Data Analytics in Genomics

Inbunden, Engelska, 2016

2 259 kr

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This contributed volume explores the emerging intersection between big data analytics and genomics. 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.

Produktinformation

Utgivningsdatum2016-11-01
Mått155 x 235 x 29 mm
Vikt816 g
FormatInbunden
SpråkEngelska
Antal sidor428
Upplaga16001
FörlagSpringer International Publishing AG
ISBN9783319412788

Tillhör följande kategorier

Tillämpad matematik inom Naturvetenskap och teknik
Matematisk statistik inom Naturvetenskap och teknik
Databaser inom Data och it
Biovetenskap inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och it

Ka-Chun Wong is Assistant Professor in the Department of Computer Science at City University of Hong Kong. He received his B.Eng. in Computer Engineering in 2008 and his M.Phil. degree in the Department of Computer Science and Engineering in 2010, both from United College, the Chinese University of Hong Kong. He finished his PhD at the Department of Computer Science at University of Toronto . His research interests include computational biology, bioinformatics, evolutionary computation, big data analytics, application machine learning, and interdisciplinary research.

Introduction to Statistical Methods for Integrative Analysis of Genomic Data.- Robust Methods for Expression Quantitative Trait Loci Mapping.- Causal Inference and Structure Learning of Genotype-Phenotype Networks using Genetic Variation.- Genomic Applications of the Neyman-Pearson Classification Paradigm.- Improving Re-annotation of Annotated Eukaryotic Genomes.- State-of-the-art in Smith-Waterman Protein Database Search.- A Survey of Computational Methods for Protein Function Prediction.- Genome Wide Mapping of Nucleosome Position and Histone Code Polymorphisms in Yeast.- Perspectives of Machine Learning Techniques in Big Data Mining of Cancer.- Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms.- NGC Analysis of Somatic Mutations in Cancer Genomes.- OncoMiner: A Pipeline for Bioinformatics Analysis of Exonic Sequence Variants in Cancer.- A Bioinformatics Approach for Understanding Genotype-Phenotype Correlation in Breast Cancer.

“This edited volume is intended to showcase the current research on big data analytics for genomics … . The edited volume is well-organized, structured, and topics appeared sequentially. Most of the chapters are self-contained. … this is a good collection of work in one place; I think this volume will attract a broader audience. I enjoyed reading a few chapters of the book and found them interesting and useful.” (Technometrics, Vol. 59 (2), April, 2017)