LEADER 04544nam 2201201z- 450 001 9910595077403321 005 20231214133242.0 035 $a(CKB)5680000000080752 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/92045 035 $a(EXLCZ)995680000000080752 100 $a20202209d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBioinformatics and Machine Learning for Cancer Biology 210 $aBasel$cMDPI Books$d2022 215 $a1 electronic resource (196 p.) 311 $a3-0365-4814-9 311 $a3-0365-4813-0 330 $aCancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of ?omics? technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer. 606 $aResearch & information: general$2bicssc 606 $aBiology, life sciences$2bicssc 610 $atumor mutational burden 610 $aDNA damage repair genes 610 $aimmunotherapy 610 $abiomarker 610 $abiomedical informatics 610 $abreast cancer 610 $aestrogen receptor alpha 610 $apersistent organic pollutants 610 $adrug-drug interaction networks 610 $amolecular docking 610 $aNGS 610 $actDNA 610 $aVAF 610 $aliquid biopsy 610 $afiltering 610 $avariant calling 610 $aDEGs 610 $adiagnosis 610 $aovarian cancer 610 $aPUS7 610 $aRMGs 610 $aCPA4 610 $abladder urothelial carcinoma 610 $aimmune cells 610 $aT cell exhaustion 610 $acheckpoint 610 $aarchitectural distortion 610 $aimage processing 610 $adepth-wise convolutional neural network 610 $amammography 610 $abladder cancer 610 $aAnnexin family 610 $asurvival analysis 610 $aprognostic signature 610 $atherapeutic target 610 $aR Shiny application 610 $aRNA-seq 610 $aproteomics 610 $amulti-omics analysis 610 $aT-cell acute lymphoblastic leukemia 610 $aCCLE 610 $asitagliptin 610 $athyroid cancer (THCA) 610 $apapillary thyroid cancer (PTCa) 610 $athyroidectomy 610 $ametastasis 610 $adrug resistance 610 $abiomarker identification 610 $atranscriptomics 610 $amachine learning 610 $aprediction 610 $avariable selection 610 $amajor histocompatibility complex 610 $abidirectional long short-term memory neural network 610 $adeep learning 610 $acancer 610 $aincidence 610 $amortality 610 $amodeling 610 $aforecasting 610 $aGoogle Trends 610 $aRomania 610 $aARIMA 610 $aTBATS 610 $aNNAR 615 7$aResearch & information: general 615 7$aBiology, life sciences 700 $aWan$b Shibiao$4edt$01128379 702 $aFan$b Yiping$4edt 702 $aJiang$b Chunjie$4edt 702 $aLi$b Shengli$4edt 702 $aWan$b Shibiao$4oth 702 $aFan$b Yiping$4oth 702 $aJiang$b Chunjie$4oth 702 $aLi$b Shengli$4oth 906 $aBOOK 912 $a9910595077403321 996 $aBioinformatics and Machine Learning for Cancer Biology$93035011 997 $aUNINA