LEADER 03488nam 2200781z- 450 001 9910557545803321 005 20210501 035 $a(CKB)5400000000044149 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68899 035 $a(oapen)doab68899 035 $a(EXLCZ)995400000000044149 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aStatistical Methods for the Analysis of Genomic Data 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (136 p.) 311 08$a3-03936-140-6 311 08$a3-03936-141-4 330 $aIn recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement. 606 $aMathematics and Science$2bicssc 606 $aResearch and information: general$2bicssc 610 $aBayes factor 610 $aBayesian mixed-effect model 610 $aboosting 610 $aclassification 610 $aclassification boundary 610 $aclustering analysis 610 $aconvolutional neural networks 610 $aCpG sites 610 $adeep learning 610 $aDNA methylation 610 $aexpectation-maximization algorithm 610 $afalse discovery rate control 610 $afeed-forward neural networks 610 $agaussian finite mixture model 610 $aGEE 610 $agene expression 610 $agene regulatory network 610 $agene set enrichment analysis 610 $aintegrative analysis 610 $akernel method 610 $alipid-environment interaction 610 $alongitudinal lipidomics study 610 $amachine learning 610 $amultiple cancer types 610 $an/a 610 $anetwork substructure 610 $anonparanormal graphical model 610 $aomics data 610 $aOrdinal responses 610 $apenalized variable selection 610 $aprognosis modeling 610 $aRNA-seq 610 $auncertainty 615 7$aMathematics and Science 615 7$aResearch and information: general 700 $aJiang$b Hui$4edt$01312123 702 $aHe$b Zhi$4edt 702 $aJiang$b Hui$4oth 702 $aHe$b Zhi$4oth 906 $aBOOK 912 $a9910557545803321 996 $aStatistical Methods for the Analysis of Genomic Data$93030716 997 $aUNINA