06358nam 2201957z- 450 9910367743403321202102113-03921-789-5(CKB)4100000010106283(oapen)https://directory.doabooks.org/handle/20.500.12854/41042(oapen)doab41042(EXLCZ)99410000001010628320202102d2019 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierApplication of Bioinformatics in CancersMDPI - Multidisciplinary Digital Publishing Institute20191 online 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.Biotechnologybicsscactivation induced deaminaseAID/APOBECalternative splicinganti-cancerartificial intelligencebioinformaticsBioinformatics toolbiomarker discoverybiomarker signaturebiomarkersbiostatisticsbrainbrain metastasesbreast cancerbreast cancer detectionbreast cancer prognosisBufadienolide-like chemicalscancercancer biomarkercancer biomarkerscancer CRISPRcancer modelingcancer prognosiscancer treatmentcancer-related pathwayscell-free DNAchemotherapycirculating tumor DNA (ctDNA)classificationclinical/environmental factorscolorectal cancercomorbidity scoreComputational Immunologyconcatenated deep featurecopy number aberrationcopy number variationcurationcurative surgerydatasetsdecision support systemsdeep learningdenoising autoencodersdifferential gene expression analysisdiseases genesDNADNA sequence profiledrug resistanceepigeneticserlotinibestrogenextreme learningfalse discovery ratefeature extraction and interpretationfeature selectionfirehosefunctional analysisgefitinibgene expression analysisgene inactivation biomarkersgene loss biomarkersgene signature extractiongenomic instabilityGEO DataSetshead and neck cancerhealth strengthening herbhierarchical clustering analysishigh-throughput analysishistopathological imaginghistopathological imaging featuresHNSCChormone sensitive cancersHPhTERTimagingindependent prognostic powerinteractionintratumor heterogeneityknockoffsKRAS mutationlocoregionally advancedmachine learningmeta-analysismethylationmicroarraymiRNAmiRNAsmitochondrial metabolismmixture of normal distributionsmolecular mechanismmolecular subtypesMonte Carlomortalitymultiple-biomarkersmutable motifmutationNeoantigen Predictionnetwork analysisNetwork Analysisnetwork pharmacologynetwork targetneurological disordersnext generation sequencingobserved survival intervalomicsomics profilesoral cancerovarian canceroverall survivalpancreatic cancerpathophysiologyPD-L1precision medicinepredictive modelproteinR packageRNAself-organizing mapsingle-biomarkerssingle-cell sequencingskin cutaneous melanomasomatic mutationStARsteroidogenic enzymessurvival analysisTCGATCGA miningtelomerasetelomeresThe Cancer Genome Atlastraditional Chinese medicinetranscriptional signaturestreatment de-escalationtumortumor infiltrating lymphocytestumor microenvironmentvariable selectionBiotechnologyBrenner J. Chadauth1292380BOOK9910367743403321Application of Bioinformatics in Cancers3022234UNINA