AIMC Topic: Gene Regulatory Networks

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Screening of mitochondrial-related biomarkers connected with immune infiltration for acute respiratory distress syndrome through WGCNA and machine learning.

Medicine
Septic acute respiratory distress syndrome (ARDS) is a complex and noteworthy type, but its molecular mechanism has not been fully elucidated. The aim is to explore specific biomarkers to diagnose sepsis-induced ARDS. Gene expression data of sepsis a...

Exploring Mechanisms and Biomarkers of Breast Cancer Invasion and Migration: An Explainable Gene-Pathway-Compounds Neural Network.

Cancer medicine
BACKGROUNDS: Exploring the molecular features that drive breast cancer invasion and migration remains an important biological and clinical challenge. In recent years, the use of interpretable machine learning models has enhanced our understanding of ...

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Nucleic acids research
Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunit...

Constructing a Prognostic Model for Subtypes of Colorectal Cancer Based on Machine Learning and Immune Infiltration-Related Genes.

Journal of cellular and molecular medicine
This study constructed a prognostic model combining machine learning-based immune infiltration-related genes in each CRC subtype. We used publicly accessible gene expression data and clinical information on colorectal cancer patients. Integrated bioi...

scTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies.

Nucleic acids research
Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-...

Construction of a prognostic model for endometrial cancer related to programmed cell death using WGCNA and machine learning algorithms.

Frontiers in immunology
BACKGROUND: Programmed cell death (PCD) refers to a regulated and active process of cellular demise, initiated by specific biological signals. PCD plays a crucial role in the development, progression, and drug resistance of uterine corpus endometrial...

Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.

International journal of chronic obstructive pulmonary disease
BACKGROUND: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanisms remain unclear. This study aimed to explore shared diagnostic genes, potential mechanisms, and th...

Bioinformatics meets machine learning: identifying circulating biomarkers for vitiligo across blood and tissues.

Frontiers in immunology
BACKGROUND: Vitiligo is a skin disorder characterized by the progressive loss of pigmentation in the skin and mucous membranes. The exact aetiology and pathogenesis of vitiligo remain incompletely understood.

Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning.

PloS one
Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder. The inflammatory response is essential in the pathogenesis and progression of PD. The goal of this study is to combine bioinformatics and machine learning to screen for...