AIMC Topic: DNA Methylation
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Predicting cancer origins with a DNA methylation-based deep neural network model.
PloS one
May 8, 2020
Cancer origin determination combined with site-specific treatment of metastatic cancer patients is critical to improve patient outcomes. Existing pathology and gene expression-based techniques often have limited performance. In this study, we develop...
Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification.
Clinical epigenetics
Apr 3, 2020
BACKGROUND: Machine learning is a sub-field of artificial intelligence, which utilises large data sets to make predictions for future events. Although most algorithms used in machine learning were developed as far back as the 1950s, the advent of big...
Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.
Genomics
Mar 29, 2020
Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understan...
Identification of Methylated Gene Biomarkers in Patients with Alzheimer's Disease Based on Machine Learning.
BioMed research international
Mar 26, 2020
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder and characterized by the cognitive impairments. It is essential to identify potential gene biomarkers for AD pathology.
Alzheimer Disease
Patients
Peptide Hydrolases
Carrier Proteins
Humans
DNA-Binding Proteins
Machine Learning
Genetic Markers
Gene Expression Regulation, Neoplastic
Protein Tyrosine Phosphatases
Transcription Factors
Mitochondrial Proteins
DNA Methylation
Microfilament Proteins
Cyclin-Dependent Kinase Inhibitor p57
MethylNet: an automated and modular deep learning approach for DNA methylation analysis.
BMC bioinformatics
Mar 17, 2020
BACKGROUND: DNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from mul...
Collective effects of long-range DNA methylations predict gene expressions and estimate phenotypes in cancer.
Scientific reports
Mar 3, 2020
DNA methylation of various genomic regions has been found to be associated with gene expression in diverse biological contexts. However, most genome-wide studies have focused on the effect of (1) methylation in cis, not in trans and (2) a single CpG,...
Invited Review: DNA methylation-based classification of paediatric brain tumours.
Neuropathology and applied neurobiology
Feb 19, 2020
DNA methylation-based machine learning algorithms represent powerful diagnostic tools that are currently emerging for several fields of tumour classification. For various reasons, paediatric brain tumours have been the main driving forces behind this...
Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning.
Clinical cancer research : an official journal of the American Association for Cancer Research
Jan 28, 2020
PURPOSE: Pancreatic cancer remains a disease of high mortality despite advanced diagnostic techniques. Mucins (MUC) play crucial roles in carcinogenesis and tumor invasion in pancreatic cancers. MUC1 and MUC4 expression are related to the aggressive ...
Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics.
Scientific reports
Jan 27, 2020
Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need to molecularly distinguish between different clinically relevant pancreatic cancer subtypes. To add...
Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data.
Nature protocols
Jan 13, 2020
DNA methylation data-based precision cancer diagnostics is emerging as the state of the art for molecular tumor classification. Standards for choosing statistical methods with regard to well-calibrated probability estimates for these typically highly...
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