04544nam 2201201z- 450 991059507740332120231214133242.0(CKB)5680000000080752(oapen)https://directory.doabooks.org/handle/20.500.12854/92045(EXLCZ)99568000000008075220202209d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierBioinformatics and Machine Learning for Cancer BiologyBaselMDPI Books20221 electronic resource (196 p.)3-0365-4814-9 3-0365-4813-0 Cancer 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.Research & information: generalbicsscBiology, life sciencesbicssctumor mutational burdenDNA damage repair genesimmunotherapybiomarkerbiomedical informaticsbreast cancerestrogen receptor alphapersistent organic pollutantsdrug-drug interaction networksmolecular dockingNGSctDNAVAFliquid biopsyfilteringvariant callingDEGsdiagnosisovarian cancerPUS7RMGsCPA4bladder urothelial carcinomaimmune cellsT cell exhaustioncheckpointarchitectural distortionimage processingdepth-wise convolutional neural networkmammographybladder cancerAnnexin familysurvival analysisprognostic signaturetherapeutic targetR Shiny applicationRNA-seqproteomicsmulti-omics analysisT-cell acute lymphoblastic leukemiaCCLEsitagliptinthyroid cancer (THCA)papillary thyroid cancer (PTCa)thyroidectomymetastasisdrug resistancebiomarker identificationtranscriptomicsmachine learningpredictionvariable selectionmajor histocompatibility complexbidirectional long short-term memory neural networkdeep learningcancerincidencemortalitymodelingforecastingGoogle TrendsRomaniaARIMATBATSNNARResearch & information: generalBiology, life sciencesWan Shibiaoedt1128379Fan YipingedtJiang ChunjieedtLi ShengliedtWan ShibiaoothFan YipingothJiang ChunjieothLi ShengliothBOOK9910595077403321Bioinformatics and Machine Learning for Cancer Biology3035011UNINA