Application of Bioinformatics in Cancers

• Autore: Brenner J. Chad
• Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute, 2019
• Descrizione fisica: 1 online resource (418 p.)
• Soggetto topico: Biotechnology
• Soggetto non controllato: activation induced deaminase; AID/APOBEC; alternative splicing; anti-cancer; artificial intelligence; bioinformatics; Bioinformatics tool; biomarker discovery; biomarker signature; biomarkers; biostatistics; brain; brain metastases; breast cancer; breast cancer detection; breast cancer prognosis; Bufadienolide-like chemicals; cancer; cancer biomarker; cancer biomarkers; cancer CRISPR; cancer modeling; cancer prognosis; cancer treatment; cancer-related pathways; cell-free DNA; chemotherapy; circulating tumor DNA (ctDNA); classification; clinical/environmental factors; colorectal cancer; comorbidity score; Computational Immunology; concatenated deep feature; copy number aberration; copy number variation; curation; curative surgery; datasets; decision support systems; deep learning; denoising autoencoders; differential gene expression analysis; diseases genes; DNA; DNA sequence profile; drug resistance; epigenetics; erlotinib; estrogen; extreme learning; false discovery rate; feature extraction and interpretation; feature selection; firehose; functional analysis; gefitinib; gene expression analysis; gene inactivation biomarkers; gene loss biomarkers; gene signature extraction; genomic instability; GEO DataSets; head and neck cancer; health strengthening herb; hierarchical clustering analysis; high-throughput analysis; histopathological imaging; histopathological imaging features; HNSCC; hormone sensitive cancers; HP; hTERT; imaging; independent prognostic power; interaction; intratumor heterogeneity; knockoffs; KRAS mutation; locoregionally advanced; machine learning; meta-analysis; methylation; microarray; miRNA; miRNAs; mitochondrial metabolism; mixture of normal distributions; molecular mechanism; molecular subtypes; Monte Carlo; mortality; multiple-biomarkers; mutable motif; mutation; Neoantigen Prediction; network analysis; Network Analysis; network pharmacology; network target; neurological disorders; next generation sequencing; observed survival interval; omics; omics profiles; oral cancer; ovarian cancer; overall survival; pancreatic cancer; pathophysiology; PD-L1; precision medicine; predictive model; protein; R package; RNA; self-organizing map; single-biomarkers; single-cell sequencing; skin cutaneous melanoma; somatic mutation; StAR; steroidogenic enzymes; survival analysis; TCGA; TCGA mining; telomerase; telomeres; The Cancer Genome Atlas; traditional Chinese medicine; transcriptional signatures; treatment de-escalation; tumor; tumor infiltrating lymphocytes; tumor microenvironment; variable selection
• Sommario/riassunto: 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.
• Titolo autorizzato: Application of Bioinformatics in Cancers
• ISBN: 3-03921-789-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910367743403321
• Lo trovi qui: Univ. Federico II