000 06433naaaa2201885uu 4500
001 https://directory.doabooks.org/handle/20.500.12854/41042
005 20220219194558.0
020 _abooks978-3-03921-789-2
020 _a9783039217892
020 _a9783039217885
024 7 _a10.3390/books978-3-03921-789-2
_cdoi
041 0 _aEnglish
042 _adc
100 1 _aBrenner, J. Chad
_4auth
245 1 0 _aApplication of Bioinformatics in Cancers
260 _bMDPI - Multidisciplinary Digital Publishing Institute
_c2019
300 _a1 electronic resource (418 p.)
506 0 _aOpen Access
_2star
_fUnrestricted online access
520 _aThis 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.
540 _aCreative Commons
_fhttps://creativecommons.org/licenses/by-nc-nd/4.0/
_2cc
_4https://creativecommons.org/licenses/by-nc-nd/4.0/
546 _aEnglish
653 _acancer treatment
653 _aextreme learning
653 _aindependent prognostic power
653 _aAID/APOBEC
653 _aHP
653 _agene inactivation biomarkers
653 _abiomarker discovery
653 _achemotherapy
653 _aartificial intelligence
653 _aepigenetics
653 _acomorbidity score
653 _adenoising autoencoders
653 _aprotein
653 _asingle-biomarkers
653 _agene signature extraction
653 _ahigh-throughput analysis
653 _aconcatenated deep feature
653 _afeature selection
653 _adifferential gene expression analysis
653 _acolorectal cancer
653 _aovarian cancer
653 _amultiple-biomarkers
653 _agefitinib
653 _acancer biomarkers
653 _aclassification
653 _acancer biomarker
653 _amutation
653 _ahierarchical clustering analysis
653 _aHNSCC
653 _acell-free DNA
653 _anetwork analysis
653 _adrug resistance
653 _ahTERT
653 _avariable selection
653 _aKRAS mutation
653 _asingle-cell sequencing
653 _anetwork target
653 _askin cutaneous melanoma
653 _atelomeres
653 _aNeoantigen Prediction
653 _adatasets
653 _aclinical/environmental factors
653 _aStAR
653 _aPD-L1
653 _amiRNA
653 _acirculating tumor DNA (ctDNA)
653 _afalse discovery rate
653 _apredictive model
653 _aComputational Immunology
653 _abrain metastases
653 _aobserved survival interval
653 _anext generation sequencing
653 _abrain
653 _amachine learning
653 _acancer prognosis
653 _acopy number aberration
653 _amutable motif
653 _asteroidogenic enzymes
653 _atumor
653 _amortality
653 _atumor microenvironment
653 _asomatic mutation
653 _atranscriptional signatures
653 _aomics profiles
653 _amitochondrial metabolism
653 _aBufadienolide-like chemicals
653 _acancer-related pathways
653 _aintratumor heterogeneity
653 _aestrogen
653 _alocoregionally advanced
653 _aRNA
653 _afeature extraction and interpretation
653 _atreatment de-escalation
653 _aactivation induced deaminase
653 _aknockoffs
653 _aR package
653 _acopy number variation
653 _agene loss biomarkers
653 _acancer CRISPR
653 _aoverall survival
653 _ahistopathological imaging
653 _aself-organizing map
653 _aNetwork Analysis
653 _aoral cancer
653 _abiostatistics
653 _afirehose
653 _aBioinformatics tool
653 _aalternative splicing
653 _abiomarkers
653 _adiseases genes
653 _ahistopathological imaging features
653 _aimaging
653 _aTCGA
653 _adecision support systems
653 _aThe Cancer Genome Atlas
653 _amolecular subtypes
653 _amolecular mechanism
653 _aomics
653 _acurative surgery
653 _anetwork pharmacology
653 _amethylation
653 _abioinformatics
653 _aneurological disorders
653 _aprecision medicine
653 _acancer modeling
653 _amiRNAs
653 _abreast cancer detection
653 _afunctional analysis
653 _abiomarker signature
653 _aanti-cancer
653 _ahormone sensitive cancers
653 _adeep learning
653 _aDNA sequence profile
653 _apancreatic cancer
653 _atelomerase
653 _aMonte Carlo
653 _amixture of normal distributions
653 _asurvival analysis
653 _atumor infiltrating lymphocytes
653 _acuration
653 _apathophysiology
653 _aGEO DataSets
653 _ahead and neck cancer
653 _agene expression analysis
653 _aerlotinib
653 _ameta-analysis
653 _atraditional Chinese medicine
653 _abreast cancer
653 _aTCGA mining
653 _abreast cancer prognosis
653 _amicroarray
653 _aDNA
653 _ainteraction
653 _ahealth strengthening herb
653 _acancer
653 _agenomic instability
856 4 0 _awww.oapen.org
_uhttps://mdpi.com/books/pdfview/book/1821
_70
_zDOAB: download the publication
856 4 0 _awww.oapen.org
_uhttps://directory.doabooks.org/handle/20.500.12854/41042
_70
_zDOAB: description of the publication
999 _c39522
_d39522