LEADER 02291oam 2200469I 450 001 9910162857403321 005 20230612184505.0 010 $a1-4822-5862-5 010 $a1-315-37226-6 010 $a1-4822-5860-9 024 7 $a10.1201/9781315372266 035 $a(CKB)3710000001021899 035 $a(MiAaPQ)EBC4778632 035 $a(OCoLC)967412430 035 $a(EXLCZ)993710000001021899 100 $a20180706h20172017 uy 0 101 0 $aeng 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aStatistical modeling and machine learning for molecular biology /$fAlan Moses, University of Toronto, Canada 210 1$aBoca Raton :$cCRC Press,$d[2017] 210 4$dİ2017 215 $a1 online resource 225 1 $aChapman & Hall/CRC mathematical and computational biology series 311 $a1-138-40721-6 311 $a1-4822-5859-5 327 $asection 1. Overview -- section 2. Clustering -- section 3. Regression -- section 4. Classification. 330 $aMolecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics. 410 0$aChapman and Hall/CRC mathematical & computational biology series. 606 $aMolecular biology$xStatistical methods 606 $aMolecular biology$xData processing 615 0$aMolecular biology$xStatistical methods. 615 0$aMolecular biology$xData processing. 676 $a572.8 700 $aMoses$b Alan$01212883 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910162857403321 996 $aStatistical modeling and machine learning for molecular biology$92801009 997 $aUNINA