AIMC Topic: Gene Regulatory Networks
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Deep Learning in Gene Regulatory Network Inference: A Survey.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
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
Dec 10, 2024
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
Dec 6, 2024
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
Dec 4, 2024
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...
Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration.
Frontiers in immunology
Dec 4, 2024
BACKGROUND: Low back pain resulting from intervertebral disc degeneration (IVDD) represents a significant global social problem. There are notable differences in the distribution of lymphatic vessels (LV) in normal and pathological intervertebral dis...
Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation.
Frontiers in immunology
Dec 4, 2024
BACKGROUND: Diabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision impairment. Understanding the molecular mechanisms, particularly PANoptosis, underlying DR is crucial for identifying potential biomarkers and therape...
Regulated Cell Death
Gene Regulatory Networks
Nomograms
Human Umbilical Vein Endothelial Cells
Biomarkers
ROC Curve
Humans
Molecular Targeted Therapy
Random Forest
RNA-Seq
Fatty Acid Synthase, Type I
Diabetic Retinopathy
Memory T Cells
CD4-Positive T-Lymphocytes
Phospholipid Transfer Proteins
Machine Learning
Gene Expression Regulation
Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction.
Biological psychiatry
Nov 29, 2024
BACKGROUND: Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional...
Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning.
Hereditas
Nov 28, 2024
PURPOSE: Mitochondria are involved in septic shock and inflammatory response syndrome, which severely affects the life security of patients. It is necessary to recognize and explore the immune-mitochondrial genes in septic shock.
Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics analysis and machine learning.
Scientific reports
Nov 27, 2024
Colorectal polyps are precursors of colorectal cancer. Metabolic dysfunction associated steatohepatitis (MASH) is one of metabolic dysfunction associated fatty liver disease (MAFLD) phenotypic manifestations. Much evidence has suggested an associatio...
EfficientNet-resDDSC: A Hybrid Deep Learning Model Integrating Residual Blocks and Dilated Convolutions for Inferring Gene Causality in Single-Cell Data.
Interdisciplinary sciences, computational life sciences
Nov 22, 2024
Gene Regulatory Networks (GRNs) reveal complex interactions between genes in organisms, crucial for understanding the life system's operation. The rapid development of biotechnology, especially single-cell RNA sequencing (scRNA-seq), has generated a ...
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