LEADER 03441nam 22007215 450 001 9910438136003321 005 20251116165604.0 010 $a9781461464464 010 $a1461464463 024 7 $a10.1007/978-1-4614-6446-4 035 $a(CKB)2560000000102854 035 $a(EBL)1697681 035 $a(OCoLC)842137782 035 $a(SSID)ssj0000906270 035 $a(PQKBManifestationID)11536121 035 $a(PQKBTitleCode)TC0000906270 035 $a(PQKBWorkID)10930518 035 $a(PQKB)11386567 035 $a(DE-He213)978-1-4614-6446-4 035 $a(MiAaPQ)EBC1697681 035 $a(PPN)169136213 035 $a(EXLCZ)992560000000102854 100 $a20130427d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian Networks in R $ewith Applications in Systems Biology /$fby Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre 205 $a1st ed. 2013. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2013. 215 $a1 online resource (168 p.) 225 1 $aUse R!,$x2197-5744 ;$v48 300 $aDescription based upon print version of record. 311 08$a9781461464457 311 08$a1461464455 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Bayesian Networks in the Absence of Temporal Information -- Bayesian Networds in the Presence of Temporal Information -- Bayesian Network Inference Algorithms -- Parallel Computing for Bayesian Networks -- Solutions -- Index -- References. 330 $aBayesian Networks in R with Applications in Systems Biology introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is gradually increased across the chapters with exercises and solutions for enhanced understanding and hands-on experimentation of key concepts. Applications focus on systems biology with emphasis on modeling pathways and signaling mechanisms from high throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regards as exemplified by their ability to discover new associations while validating known ones. It is also expected that the prevalence of publicly available high-throughput biological and healthcare data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book. 410 0$aUse R!,$x2197-5744 ;$v48 606 $aMathematical statistics$xData processing 606 $aStatistics 606 $aCompilers (Computer programs) 606 $aStatistics and Computing 606 $aStatistical Theory and Methods 606 $aCompilers and Interpreters 615 0$aMathematical statistics$xData processing. 615 0$aStatistics. 615 0$aCompilers (Computer programs) 615 14$aStatistics and Computing. 615 24$aStatistical Theory and Methods. 615 24$aCompilers and Interpreters. 676 $a519.542 700 $aNagarajan$b Radhakrishnan$01063016 701 $aScutari$b Marco$01757990 701 $aLe?bre$b Sophie$01757991 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438136003321 996 $aBayesian networks in R$94196023 997 $aUNINA