LEADER 03460nam 2200661 450 001 9910453636303321 005 20200520144314.0 010 $a981-4551-01-5 035 $a(CKB)2550000001191462 035 $a(EBL)3051317 035 $a(OCoLC)922951932 035 $a(SSID)ssj0001039941 035 $a(PQKBManifestationID)12468532 035 $a(PQKBTitleCode)TC0001039941 035 $a(PQKBWorkID)10990325 035 $a(PQKB)10231415 035 $a(MiAaPQ)EBC3051317 035 $a(WSP)00008898 035 $a(Au-PeEL)EBL3051317 035 $a(CaPaEBR)ebr10832713 035 $a(CaONFJC)MIL570885 035 $a(EXLCZ)992550000001191462 100 $a20130917h20142014 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBiological data mining and its applications in healthcare /$feditors, Xiaoli Li (A*STAR, Singapore & Nanyang Technological University, Singapore), See-Kiong Ng (A*STAR, Singapore), Jason T.L. Wang (New Jersey Institute of Technology, USA) 210 1$aNew Jersey :$cWorld Scientific,$d[2014] 210 4$dİ2014 215 $a1 online resource (437 p.) 225 1 $aScience, engineering, and biology infomatics ;$vvolume 8 300 $aDescription based upon print version of record. 311 $a981-4551-00-7 311 $a1-306-39634-4 320 $aIncludes bibliographical references and index. 327 $apart I. Sequence analysis -- part II. Biological network mining -- part III. Classification, trend analysis and 3D medical images -- part IV. Text mining and its biomedical applications. 330 $aBiologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains. 410 0$aScience, engineering, and biology infomatics ;$vv. 8. 606 $aMedical informatics 606 $aBioinformatics 606 $aData mining 608 $aElectronic books. 615 0$aMedical informatics. 615 0$aBioinformatics. 615 0$aData mining. 676 $a610.285 701 $aLi$b Xiao-Li$f1969-$0941637 701 $aNg$b See-Kiong$0941638 701 $aWang$b Jason T. L$0931445 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453636303321 996 $aBiological data mining and its applications in healthcare$92124216 997 $aUNINA