LEADER 04291nam 22007215 450 001 9910298966703321 005 20230119011645.0 010 $a1-4939-1381-6 024 7 $a10.1007/978-1-4939-1381-7 035 $a(CKB)3710000000227338 035 $a(EBL)1964972 035 $a(OCoLC)890140901 035 $a(SSID)ssj0001338431 035 $a(PQKBManifestationID)11710522 035 $a(PQKBTitleCode)TC0001338431 035 $a(PQKBWorkID)11344375 035 $a(PQKB)10962485 035 $a(MiAaPQ)EBC1964972 035 $a(DE-He213)978-1-4939-1381-7 035 $a(PPN)18062265X 035 $a(EXLCZ)993710000000227338 100 $a20140830d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCancer Bioinformatics /$fby Ying Xu, Juan Cui, David Puett 205 $a1st ed. 2014. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2014. 215 $a1 online resource (386 p.) 300 $aDescription based upon print version of record. 311 $a1-4939-1380-8 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aBasic cancer biology -- Omic data, information derivable and computational needs -- Cancer classification and molecular signature identification -- Understanding cancer at the genomic level -- Elucidation of cancer divers through comparative omic analyses -- Hyaluronic acid: A key facilitator of cancer evolution -- Multiple routes for survival: Understanding how cancer evades apoptosis -- Cancer development in competitive and hostile environments -- Cell proliferation from regulated to deregulated state via epigenomic responses -- Understanding cancer invasion and metastasis -- Cancer after metastasis: The second transformation -- Searching for cancer biomarkers in human body fluids -- In silico investigation of cancer using publicly available data -- Understanding cancer as an evolving complex system: our perspective. 330 $aThis book provides a framework for computational researchers studying the basics of cancer through comparative analyses of omic data. It discusses how key cancer pathways can be analyzed and discovered to derive new insights into the disease and identifies diagnostic and prognostic markers for cancer. Chapters explain the basic cancer biology and how cancer develops, including the many potential survival routes. The examination of gene-expression patterns uncovers commonalities across multiple cancers and specific characteristics of individual cancer types. The authors also treat cancer as an evolving complex system, explore future case studies, and summarize the essential online data sources. Cancer Bioinformatics is designed for practitioners and researchers working in cancer research and bioinformatics. It is also suitable as a secondary textbook for advanced-level students studying computer science, biostatistics or biomedicine. 606 $aBioinformatics 606 $aCancer$xResearch 606 $aSystems biology 606 $aMedicine 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aCancer Research$3https://scigraph.springernature.com/ontologies/product-market-codes/B11001 606 $aSystems Biology$3https://scigraph.springernature.com/ontologies/product-market-codes/L15010 606 $aBiomedicine, general$3https://scigraph.springernature.com/ontologies/product-market-codes/B0000X 615 0$aBioinformatics. 615 0$aCancer$xResearch. 615 0$aSystems biology. 615 0$aMedicine. 615 14$aComputational Biology/Bioinformatics. 615 24$aCancer Research. 615 24$aSystems Biology. 615 24$aBiomedicine, general. 676 $a004 676 $a570 676 $a570285 676 $a610 700 $aXu$b Ying$f1960-$4aut$4http://id.loc.gov/vocabulary/relators/aut$01165726 702 $aCui$b Juan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aPuett$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910298966703321 996 $aCancer Bioinformatics$92999038 997 $aUNINA