LEADER 01175nam 2200409 450 001 9910153219103321 005 20230810001417.0 010 $a1-78702-983-2 010 $a1-78702-982-4 035 $a(CKB)3710000000960484 035 $a(MiAaPQ)EBC4745395 035 $a(EXLCZ)993710000000960484 100 $a20161208h20172017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aNew Zealand /$fSue Butler and Ljiljana Ortolja-Baird 210 1$aLondon, [England] :$cKuperard,$d[2017] 210 4$dİ[2017] 215 $a1 online resource (169 pages) $cillustrations, tables, maps 225 1 $aCulture Smart! 311 $a1-85733-856-1 320 $aIncludes bibliographical references and index. 410 0$aCulture smart! 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The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. 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