Application of Bioinformatics in Cancers
Brenner, J. Chad
Application of Bioinformatics in Cancers - MDPI - Multidisciplinary Digital Publishing Institute 2019 - 1 electronic resource (418 p.)
Open Access
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.
Creative Commons
English
books978-3-03921-789-2 9783039217892 9783039217885
10.3390/books978-3-03921-789-2 doi
cancer treatment extreme learning independent prognostic power AID/APOBEC HP gene inactivation biomarkers biomarker discovery chemotherapy artificial intelligence epigenetics comorbidity score denoising autoencoders protein single-biomarkers gene signature extraction high-throughput analysis concatenated deep feature feature selection differential gene expression analysis colorectal cancer ovarian cancer multiple-biomarkers gefitinib cancer biomarkers classification cancer biomarker mutation hierarchical clustering analysis HNSCC cell-free DNA network analysis drug resistance hTERT variable selection KRAS mutation single-cell sequencing network target skin cutaneous melanoma telomeres Neoantigen Prediction datasets clinical/environmental factors StAR PD-L1 miRNA circulating tumor DNA (ctDNA) false discovery rate predictive model Computational Immunology brain metastases observed survival interval next generation sequencing brain machine learning cancer prognosis copy number aberration mutable motif steroidogenic enzymes tumor mortality tumor microenvironment somatic mutation transcriptional signatures omics profiles mitochondrial metabolism Bufadienolide-like chemicals cancer-related pathways intratumor heterogeneity estrogen locoregionally advanced RNA feature extraction and interpretation treatment de-escalation activation induced deaminase knockoffs R package copy number variation gene loss biomarkers cancer CRISPR overall survival histopathological imaging self-organizing map Network Analysis oral cancer biostatistics firehose Bioinformatics tool alternative splicing biomarkers diseases genes histopathological imaging features imaging TCGA decision support systems The Cancer Genome Atlas molecular subtypes molecular mechanism omics curative surgery network pharmacology methylation bioinformatics neurological disorders precision medicine cancer modeling miRNAs breast cancer detection functional analysis biomarker signature anti-cancer hormone sensitive cancers deep learning DNA sequence profile pancreatic cancer telomerase Monte Carlo mixture of normal distributions survival analysis tumor infiltrating lymphocytes curation pathophysiology GEO DataSets head and neck cancer gene expression analysis erlotinib meta-analysis traditional Chinese medicine breast cancer TCGA mining breast cancer prognosis microarray DNA interaction health strengthening herb cancer genomic instability
Application of Bioinformatics in Cancers - MDPI - Multidisciplinary Digital Publishing Institute 2019 - 1 electronic resource (418 p.)
Open Access
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.
Creative Commons
English
books978-3-03921-789-2 9783039217892 9783039217885
10.3390/books978-3-03921-789-2 doi
cancer treatment extreme learning independent prognostic power AID/APOBEC HP gene inactivation biomarkers biomarker discovery chemotherapy artificial intelligence epigenetics comorbidity score denoising autoencoders protein single-biomarkers gene signature extraction high-throughput analysis concatenated deep feature feature selection differential gene expression analysis colorectal cancer ovarian cancer multiple-biomarkers gefitinib cancer biomarkers classification cancer biomarker mutation hierarchical clustering analysis HNSCC cell-free DNA network analysis drug resistance hTERT variable selection KRAS mutation single-cell sequencing network target skin cutaneous melanoma telomeres Neoantigen Prediction datasets clinical/environmental factors StAR PD-L1 miRNA circulating tumor DNA (ctDNA) false discovery rate predictive model Computational Immunology brain metastases observed survival interval next generation sequencing brain machine learning cancer prognosis copy number aberration mutable motif steroidogenic enzymes tumor mortality tumor microenvironment somatic mutation transcriptional signatures omics profiles mitochondrial metabolism Bufadienolide-like chemicals cancer-related pathways intratumor heterogeneity estrogen locoregionally advanced RNA feature extraction and interpretation treatment de-escalation activation induced deaminase knockoffs R package copy number variation gene loss biomarkers cancer CRISPR overall survival histopathological imaging self-organizing map Network Analysis oral cancer biostatistics firehose Bioinformatics tool alternative splicing biomarkers diseases genes histopathological imaging features imaging TCGA decision support systems The Cancer Genome Atlas molecular subtypes molecular mechanism omics curative surgery network pharmacology methylation bioinformatics neurological disorders precision medicine cancer modeling miRNAs breast cancer detection functional analysis biomarker signature anti-cancer hormone sensitive cancers deep learning DNA sequence profile pancreatic cancer telomerase Monte Carlo mixture of normal distributions survival analysis tumor infiltrating lymphocytes curation pathophysiology GEO DataSets head and neck cancer gene expression analysis erlotinib meta-analysis traditional Chinese medicine breast cancer TCGA mining breast cancer prognosis microarray DNA interaction health strengthening herb cancer genomic instability