AIMC Topic: DNA Methylation

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BiLSTM-5mC: A Bidirectional Long Short-Term Memory-Based Approach for Predicting 5-Methylcytosine Sites in Genome-Wide DNA Promoters.

Molecules (Basel, Switzerland)
An important reason of cancer proliferation is the change in DNA methylation patterns, characterized by the localized hypermethylation of the promoters of tumor-suppressor genes together with an overall decrease in the level of 5-methylcytosine (5mC)...

Unsupervised learning of cross-modal mappings in multi-omics data for survival stratification of gastric cancer.

Future oncology (London, England)
This study presents a survival stratification model based on multi-omics integration using bidirectional deep neural networks (BiDNNs) in gastric cancer. Based on the survival-related representation features yielded by BiDNNs through integrating tr...

Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach.

BMC bioinformatics
BACKGROUND: Deep learning is an active bioinformatics artificial intelligence field that is useful in solving many biological problems, including predicting altered epigenetics such as DNA methylation regions. Deep learning (DL) can learn an informat...

Combination of artificial intelligence-based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis.

Journal of clinical laboratory analysis
BACKGROUND AND AIM: Gastrointestinal endoscopy and biopsy-based pathological findings are needed to diagnose early gastric cancer. However, the information of biopsy specimen is limited because of the topical procedure; therefore, pathology doctors s...

DNA methylation-based classification of malformations of cortical development in the human brain.

Acta neuropathologica
Malformations of cortical development (MCD) comprise a broad spectrum of structural brain lesions frequently associated with epilepsy. Disease definition and diagnosis remain challenging and are often prone to arbitrary judgment. Molecular classifica...

Predicting physiological aging rates from a range of quantitative traits using machine learning.

Aging
It is widely thought that individuals age at different rates. A method that measures "physiological age" or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual's risk o...

A Wavelet-Based Learning Model Enhances Molecular Prognosis in Pancreatic Adenocarcinoma.

BioMed research international
Genome-wide omics technology boosts deep interrogation into the clinical prognosis and inherent mechanism of pancreatic oncology. Classic LASSO methods coequally treat all candidates, ignoring individual characteristics, thus frequently deteriorating...

Analyses of child cardiometabolic phenotype following assisted reproductive technologies using a pragmatic trial emulation approach.

Nature communications
Assisted reproductive technologies (ART) are increasingly used, however little is known about the long-term health of ART-conceived offspring. Weak selection of comparison groups and poorly characterized mechanisms impede current understanding. In a ...

Predicting drug sensitivity of cancer cells based on DNA methylation levels.

PloS one
Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular featur...

A deep learning approach to automate whole-genome prediction of diverse epigenomic modifications in plants.

The New phytologist
Epigenetic modifications function in gene transcription, RNA metabolism, and other biological processes. However, multiple factors currently limit the scientific utility of epigenomic datasets generated for plants. Here, using deep-learning approache...