TY  - GEN
AU  - Brenner,J.Chad
TI  - Application of Bioinformatics in Cancers
SN  - books978-3-03921-789-2
PY  - 2019///
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - cancer treatment
KW  - extreme learning
KW  - independent prognostic power
KW  - AID/APOBEC
KW  - HP
KW  - gene inactivation biomarkers
KW  - biomarker discovery
KW  - chemotherapy
KW  - artificial intelligence
KW  - epigenetics
KW  - comorbidity score
KW  - denoising autoencoders
KW  - protein
KW  - single-biomarkers
KW  - gene signature extraction
KW  - high-throughput analysis
KW  - concatenated deep feature
KW  - feature selection
KW  - differential gene expression analysis
KW  - colorectal cancer
KW  - ovarian cancer
KW  - multiple-biomarkers
KW  - gefitinib
KW  - cancer biomarkers
KW  - classification
KW  - cancer biomarker
KW  - mutation
KW  - hierarchical clustering analysis
KW  - HNSCC
KW  - cell-free DNA
KW  - network analysis
KW  - drug resistance
KW  - hTERT
KW  - variable selection
KW  - KRAS mutation
KW  - single-cell sequencing
KW  - network target
KW  - skin cutaneous melanoma
KW  - telomeres
KW  - Neoantigen Prediction
KW  - datasets
KW  - clinical/environmental factors
KW  - StAR
KW  - PD-L1
KW  - miRNA
KW  - circulating tumor DNA (ctDNA)
KW  - false discovery rate
KW  - predictive model
KW  - Computational Immunology
KW  - brain metastases
KW  - observed survival interval
KW  - next generation sequencing
KW  - brain
KW  - machine learning
KW  - cancer prognosis
KW  - copy number aberration
KW  - mutable motif
KW  - steroidogenic enzymes
KW  - tumor
KW  - mortality
KW  - tumor microenvironment
KW  - somatic mutation
KW  - transcriptional signatures
KW  - omics profiles
KW  - mitochondrial metabolism
KW  - Bufadienolide-like chemicals
KW  - cancer-related pathways
KW  - intratumor heterogeneity
KW  - estrogen
KW  - locoregionally advanced
KW  - RNA
KW  - feature extraction and interpretation
KW  - treatment de-escalation
KW  - activation induced deaminase
KW  - knockoffs
KW  - R package
KW  - copy number variation
KW  - gene loss biomarkers
KW  - cancer CRISPR
KW  - overall survival
KW  - histopathological imaging
KW  - self-organizing map
KW  - Network Analysis
KW  - oral cancer
KW  - biostatistics
KW  - firehose
KW  - Bioinformatics tool
KW  - alternative splicing
KW  - biomarkers
KW  - diseases genes
KW  - histopathological imaging features
KW  - imaging
KW  - TCGA
KW  - decision support systems
KW  - The Cancer Genome Atlas
KW  - molecular subtypes
KW  - molecular mechanism
KW  - omics
KW  - curative surgery
KW  - network pharmacology
KW  - methylation
KW  - bioinformatics
KW  - neurological disorders
KW  - precision medicine
KW  - cancer modeling
KW  - miRNAs
KW  - breast cancer detection
KW  - functional analysis
KW  - biomarker signature
KW  - anti-cancer
KW  - hormone sensitive cancers
KW  - deep learning
KW  - DNA sequence profile
KW  - pancreatic cancer
KW  - telomerase
KW  - Monte Carlo
KW  - mixture of normal distributions
KW  - survival analysis
KW  - tumor infiltrating lymphocytes
KW  - curation
KW  - pathophysiology
KW  - GEO DataSets
KW  - head and neck cancer
KW  - gene expression analysis
KW  - erlotinib
KW  - meta-analysis
KW  - traditional Chinese medicine
KW  - breast cancer
KW  - TCGA mining
KW  - breast cancer prognosis
KW  - microarray
KW  - DNA
KW  - interaction
KW  - health strengthening herb
KW  - cancer
KW  - genomic instability
N1  - Open Access
N2  - 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
UR  - https://mdpi.com/books/pdfview/book/1821
UR  - https://directory.doabooks.org/handle/20.500.12854/41042
ER  -