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000763578 24500 $$aBig data analytics in genomics /$$cKa-Chun Wong, editor.
000763578 264_1 $$aSwitzerland :$$bSpringer,$$c2016.
000763578 300__ $$a1 online resource.
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000763578 337__ $$acomputer$$bc$$2rdamedia
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000763578 5050_ $$aIntroduction 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.
000763578 506__ $$aAccess limited to authorized users.
000763578 520__ $$aThis 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.
000763578 588__ $$aDescription based on print version record.
000763578 650_0 $$aBig data.
000763578 650_0 $$aGenomics.
000763578 650_0 $$aData mining.
000763578 650_0 $$aQuantitative research$$xSocial aspects.
000763578 7001_ $$aWong, Ka-Chun,$$eeditor.
000763578 77608 $$iPrint version:$$tBig Data Analytics in Genomics.$$d[Place of publication not identified] : Springer-Verlag New York Inc 2016$$z9783319412788$$w(OCoLC)951761276
000763578 852__ $$bebk
000763578 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-41279-5$$zOnline Access$$91397441.1
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