AIMC Topic: DNA Methylation
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EpiBrCan-Lite: A lightweight deep learning model for breast cancer subtype classification using epigenomic data.
Computer methods and programs in biomedicine
Dec 4, 2024
BACKGROUND AND OBJECTIVES: Early breast cancer subtypes classification improves the survival rate as it facilitates prognosis of the patient. In literature this problem was prominently solved by various Machine Learning and Deep Learning techniques. ...
Clinical and Multiomic Features Differentiate Young Black and White Breast Cancer Cohorts Derived by Machine Learning Approaches.
Clinical breast cancer
Nov 28, 2024
BACKGROUND: There are documented differences in Breast cancer (BrCA) presentations and outcomes between Black and White patients. In addition to molecular factors, socioeconomic, racial, and clinical factors result in disparities in outcomes for wome...
Insights to aging prediction with AI based epigenetic clocks.
Epigenomics
Nov 25, 2024
Over the past century, human lifespan has increased remarkably, yet the inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, and chronological age, indicative of normal aging, h...
Graph Convolutional Network for AD and MCI Diagnosis Utilizing Peripheral DNA Methylation: Réseau de neurones en graphes pour le diagnostic de la MA et du TCL à l'aide de la méthylation de l'ADN périphérique.
Canadian journal of psychiatry. Revue canadienne de psychiatrie
Nov 25, 2024
OBJECTIVE: Blood DNA methylation (DNAm) alterations have been widely reported in the onset and progression of mild cognitive impairment (MCI) and Alzheimer's disease (AD); however, DNAm is underutilized as a diagnostic biomarker for these diseases. W...
Robust double machine learning model with application to omics data.
BMC bioinformatics
Nov 14, 2024
BACKGROUND: Recently, there has been a growing interest in combining causal inference with machine learning algorithms. Double machine learning model (DML), as an implementation of this combination, has received widespread attention for their experti...
AI tool for predicting MGMT methylation in glioblastoma for clinical decision support in resource limited settings.
Scientific reports
Nov 14, 2024
Glioblastoma is an aggressive brain cancer with a poor prognosis. The O6-methylguanine-DNA methyltransferase (MGMT) gene methylation status is crucial for treatment stratification, yet economic constraints often limit access. This study aims to devel...
Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches.
Scientific reports
Nov 11, 2024
Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This st...
Artificial intelligence and deep learning algorithms for epigenetic sequence analysis: A review for epigeneticists and AI experts.
Computers in biology and medicine
Nov 4, 2024
Epigenetics encompasses mechanisms that can alter the expression of genes without changing the underlying genetic sequence. The epigenetic regulation of gene expression is initiated and sustained by several mechanisms such as DNA methylation, histone...
Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.
BioFactors (Oxford, England)
Oct 11, 2024
The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognos...
A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin.
International journal of clinical oncology
Sep 18, 2024
BACKGROUND: Genome DNA methylation profiling is a promising yet costly method for cancer classification, involving substantial data. We developed an ensemble learning model to identify cancer types using methylation profiles from a limited number of ...
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