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MicroRNA as Biomarkers in Cancer Diagnostics and Therapy

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ISBN: 9783039212491 / 9783039212507 Year: Pages: 166 DOI: 10.3390/books978-3-03921-250-7 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Microbiology
Added to DOAB on : 2019-08-28 11:21:27
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This Special Issue celebrates the 25th anniversary of the discovery of the first microRNA. The size of the microRNome and complexity of animal body plans and organ systems suggests a role for microRNAs in cell fate determination and differentiation. More than 2000 sequences have been proposed to represent unique microRNA genes in humans, with an increasing number of mechanistic roles identified in developmental, physiological, and pathological processes. Thus, dysregulation of a few key microRNAs can have a profound global effect on the gene expression and molecular programs of a cell. This great potential for clinical intervention has captured the interest and imagination of researchers in many fields. However, very few fields have been as prolific as the field of cancer research. This Special Issue provides but a glimpse of the large body of literature of microRNA biology in cancer research, containing 4 original research studies and 4 review articles that focus on specific hematologic or solid tumors in disease. Collectively, these articles highlight state-of-the-art approaches and methodologies for microRNA detection in tissue, blood, and other body fluids in a range of biomarkers applications, from early cancer detection to prognosis and treatment response. The articles also address some of the challenges regarding clinical implementation.

Application of Bioinformatics in Cancers

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ISBN: 9783039217885 / 9783039217892 Year: Pages: 418 DOI: 10.3390/books978-3-03921-789-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Biotechnology
Added to DOAB on : 2019-12-09 11:49:16
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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.

Keywords

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

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MDPI - Multidisciplinary Digital Publishing Institute (2)


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CC by-nc-nd (2)


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eng (2)


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2019 (2)