06273nam 2201921z- 450 991036774340332120231214133215.03-03921-789-5(CKB)4100000010106283(oapen)https://directory.doabooks.org/handle/20.500.12854/41042(EXLCZ)99410000001010628320202102d2019 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierApplication of Bioinformatics in CancersMDPI - Multidisciplinary Digital Publishing Institute20191 electronic resource (418 p.)3-03921-788-7 This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. 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. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.cancer treatmentextreme learningindependent prognostic powerAID/APOBECHPgene inactivation biomarkersbiomarker discoverychemotherapyartificial intelligenceepigeneticscomorbidity scoredenoising autoencodersproteinsingle-biomarkersgene signature extractionhigh-throughput analysisconcatenated deep featurefeature selectiondifferential gene expression analysiscolorectal cancerovarian cancermultiple-biomarkersgefitinibcancer biomarkersclassificationcancer biomarkermutationhierarchical clustering analysisHNSCCcell-free DNAnetwork analysisdrug resistancehTERTvariable selectionKRAS mutationsingle-cell sequencingnetwork targetskin cutaneous melanomatelomeresNeoantigen Predictiondatasetsclinical/environmental factorsStARPD-L1miRNAcirculating tumor DNA (ctDNA)false discovery ratepredictive modelComputational Immunologybrain metastasesobserved survival intervalnext generation sequencingbrainmachine learningcancer prognosiscopy number aberrationmutable motifsteroidogenic enzymestumormortalitytumor microenvironmentsomatic mutationtranscriptional signaturesomics profilesmitochondrial metabolismBufadienolide-like chemicalscancer-related pathwaysintratumor heterogeneityestrogenlocoregionally advancedRNAfeature extraction and interpretationtreatment de-escalationactivation induced deaminaseknockoffsR packagecopy number variationgene loss biomarkerscancer CRISPRoverall survivalhistopathological imagingself-organizing mapNetwork Analysisoral cancerbiostatisticsfirehoseBioinformatics toolalternative splicingbiomarkersdiseases geneshistopathological imaging featuresimagingTCGAdecision support systemsThe Cancer Genome Atlasmolecular subtypesmolecular mechanismomicscurative surgerynetwork pharmacologymethylationbioinformaticsneurological disordersprecision medicinecancer modelingmiRNAsbreast cancer detectionfunctional analysisbiomarker signatureanti-cancerhormone sensitive cancersdeep learningDNA sequence profilepancreatic cancertelomeraseMonte Carlomixture of normal distributionssurvival analysistumor infiltrating lymphocytescurationpathophysiologyGEO DataSetshead and neck cancergene expression analysiserlotinibmeta-analysistraditional Chinese medicinebreast cancerTCGA miningbreast cancer prognosismicroarrayDNAinteractionhealth strengthening herbcancergenomic instabilityBrenner J. Chadauth1292380BOOK9910367743403321Application of Bioinformatics in Cancers3022234UNINA