AIMC Topic: Gene Regulatory Networks

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Integrating bioinformatics and machine learning to uncover lncRNA LINC00269 as a key regulator in Parkinson's disease via pyroptosis pathways.

European journal of medical research
BACKGROUND: Pyroptosis, a specific type of programmed cell death, which has become a significant factor to Parkinson's disease (PD). Concurrently, long non-coding RNAs (lncRNAs) have garnered attention for their regulatory roles in neurodegenerative ...

Machine Learning and Mendelian Randomization Reveal Molecular Mechanisms and Causal Relationships of Immune-Related Biomarkers in Periodontitis.

Mediators of inflammation
This study aimed to investigate the molecular mechanisms of periodontitis and identify key immune-related biomarkers using machine learning and Mendelian randomization (MR). Differentially expressed gene (DEG) analysis was performed on periodontitis ...

Identification and verification of the optimal feature genes of ferroptosis in thyroid-associated orbitopathy.

Frontiers in immunology
BACKGROUND: Thyroid-associated orbitopathy (TAO) is an autoimmune inflammatory disorder of the orbital adipose tissue, primarily causing oxidative stress injury and tissue remodeling in the orbital connective tissue. Ferroptosis is a form of programm...

The significance of long chain non-coding RNA signature genes in the diagnosis and management of sepsis patients, and the development of a prediction model.

Frontiers in immunology
BACKGROUND: Sepsis is a life-threatening organ dysfunction condition produced by dysregulation of the host response to infection. It is now characterized by a high clinical morbidity and mortality rate, endangering patients' lives and health. The pur...

Integration of biological data via NMF for identification of human disease-associated gene modules through multi-label classification.

PloS one
Proteins associated with multiple diseases often interact, forming disease modules that are critical for understanding disease mechanisms. This study integrates protein-protein interactions (PPIs) and Gene Ontology data using non-negative matrix fact...

Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics.

Nature cancer
Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer f...

Deep Learning in Gene Regulatory Network Inference: A Survey.

IEEE/ACM transactions on computational biology and bioinformatics
Understanding the intricate regulatory relationships among genes is crucial for comprehending the development, differentiation, and cellular response in living systems. Consequently, inferring gene regulatory networks (GRNs) based on observed data ha...

Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data.

IEEE/ACM transactions on computational biology and bioinformatics
Microarray data provide lots of information regarding gene expression levels. Due to the large amount of such data, their analysis requires sufficient computational methods for identifying and analyzing gene regulation networks; however, researchers ...

MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network.

Computers in biology and medicine
Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targeted therapies, reducing drug-related toxicities and improving patients' survival rates. Nevertheless, accurately predicting candidate cancer-druggable...

Machine learning-based analysis of programmed cell death types and key genes in intervertebral disc degeneration.

Apoptosis : an international journal on programmed cell death
Intervertebral disc degeneration (IVDD) is intricately associated with various forms of programmed cell death (PCD). Identifying key PCD types and associated genes is essential for understanding the molecular mechanisms underlying IVDD and discoverin...