AIMC Topic: Transcription Factors
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Identification of biomarkers associated with phagocytosis regulatory factors in coronary artery disease using machine learning and network analysis.
Mammalian genome : official journal of the International Mammalian Genome Society
Feb 14, 2025
BACKGROUND: Coronary artery disease (CAD) is the leading cause of death worldwide, and aberrant phagocytosis may be involved in its development. Understanding this aspect may provide new avenues for prompt CAD diagnosis.
Gene Regulatory Networks
Polymorphism, Single Nucleotide
Computational Biology
Transcription Factors
Biomarkers
Gene Expression Profiling
Protein Interaction Maps
Phagocytosis
Receptors, IgG
Toll-Like Receptor 2
Syk Kinase
Humans
Machine Learning
Databases, Genetic
MicroRNAs
Interleukin-1beta
Coronary Artery Disease
Galactose-Induced Cataracts in Rats: A Machine Learning Analysis.
International journal of medical sciences
Feb 10, 2025
Rat models are widely used to study cataracts due to their cost-effectiveness and prominent physiological and genetic similarities to humans The objective of this study was to identify genes involved in cataractogenesis due to galactose exposure in ...
A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.
Nature communications
Feb 3, 2025
Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models can connect molecular changes to cellular phenotypes, but their design lacks modeling of regulatory ...
Machine learning-driven identification of critical gene programs and key transcription factors in migraine.
The journal of headache and pain
Jan 20, 2025
BACKGROUND: Migraine is a complex neurological disorder characterized by recurrent episodes of severe headaches. Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associ...
Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning.
Nature biomedical engineering
Jan 9, 2025
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the dev...
First-line combination therapy of immunotherapy plus anti-angiogenic drug for thoracic SMARCA4-deficient undifferentiated tumors in AIDS: a case report and review of the literature.
Frontiers in immunology
Jan 9, 2025
BACKGROUND: Thoracic SMARCA4-deficient undifferentiated tumors (SMARCA4-UT) exhibit a notably aggressive phenotype, which is associated with poor patient survival outcomes. These tumors are generally resistant to conventional cytotoxic chemotherapy, ...
ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning.
Nature communications
Jan 2, 2025
Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. Here, we develop ChromatinHD, a pair of scale-adaptive models that uses the...
Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.
Science advances
Jan 1, 2025
DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory varian...
Transcription Factor Binding Site Prediction Using CnNet Approach.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Controlling the gene expression is the most important development in a living organism, which makes it easier to find different kinds of diseases and their causes. It's very difficult to know what factors control the gene expression. Transcription Fa...
Identification of diagnostic genes and the miRNA‒mRNA‒TF regulatory network in human oocyte aging via machine learning methods.
Journal of assisted reproduction and genetics
Nov 14, 2024
PURPOSE: Oocyte aging is a significant factor in the negative reproductive outcomes of older women. However, the pathogenesis of oocyte aging remains unclear. This study aimed to identify the hub genes involved in oocyte aging via bioinformatics meth...
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