LEADER 05071nam 22005775 450 001 9910148856803321 005 20231107174032.0 010 $a9783319412795 024 7 $a10.1007/978-3-319-41279-5 035 $a(CKB)3710000000918132 035 $a(DE-He213)978-3-319-41279-5 035 $a(MiAaPQ)EBC4723617 035 $a(PPN)196325544 035 $a(EXLCZ)993710000000918132 100 $a20161024d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBig Data Analytics in Genomics /$fedited by Ka-Chun Wong 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (VIII, 428 p. 70 illus., 58 illus. in color.) 311 $a3-319-41278-7 311 $a3-319-41279-5 320 $aIncludes bibliographical references at the end of each chapters. 327 $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. 330 $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. 606 $aBioinformatics 606 $aData mining 606 $aStatistics  606 $aBiomathematics 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aGenetics and Population Dynamics$3https://scigraph.springernature.com/ontologies/product-market-codes/M31010 615 0$aBioinformatics. 615 0$aData mining. 615 0$aStatistics . 615 0$aBiomathematics. 615 14$aComputational Biology/Bioinformatics. 615 24$aData Mining and Knowledge Discovery. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aGenetics and Population Dynamics. 676 $a570.285 702 $aWong$b Ka-Chun$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910148856803321 996 $aBig Data Analytics in Genomics$92069860 997 $aUNINA