AIMC Topic: Gene Regulatory Networks

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Prior knowledge-guided multilevel graph neural network for tumor risk prediction and interpretation via multi-omics data integration.

Briefings in bioinformatics
The interrelation and complementary nature of multi-omics data can provide valuable insights into the intricate molecular mechanisms underlying diseases. However, challenges such as limited sample size, high data dimensionality and differences in omi...

Screening of key immunerelated gene in Parkinsons disease based on WGCNA and machine learning.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
OBJECTIVES: Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand how certain immune-related genes contribute to the disease's development and progression. This study a...

Integration of transcriptome and machine learning to identify the potential key genes and regulatory networks affecting drip loss in pork.

Journal of animal science
Low level of drip loss (DL) is an important quality characteristic of meat with high economic value. However, the key genes and regulatory networks contributing to DL in pork remain largely unknown. To accurately identify the key genes affecting DL i...

scGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data.

GigaScience
BACKGROUND: Exploring the cellular processes of genes from the aspects of biological networks is of great interest to understanding the properties of complex diseases and biological systems. Biological networks, such as protein-protein interaction ne...

Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients.

Technology in cancer research & treatment
BACKGROUND: Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-translational protein modification. Despite increasing focus on palmitoylation, its specific implications f...

Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
BACKGROUND: There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addit...

Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models.

Current medicinal chemistry
AIMS: The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms.

Identification of Diagnosis and Typological Characteristics Associated with Ferroptosis for Ulcerative Colitis Bioinformatics and Machine Learning.

Endocrine, metabolic & immune disorders drug targets
OBJECTIVE: To investigate and validate ferroptosis genes (FRGs) in ulcerative colitis (UC) for diagnostic, subtype, and biological agent reactivity, with the goal of providing a foundation for the identification of novel therapeutic targets and the r...

Evidence from Machine Learning, Diagnostic Hub Genes in Sepsis and Diagnostic Models based on Xgboost Models, Novel Molecular Models for the Diagnosis of Sepsis.

Current medicinal chemistry
BACKGROUND: Systemic multi-organ dysfunction resulting from dysregulated immune responses in the host triggered by microbial infection or other factors is a major cause of death in sepsis, and secretory pathways play an important role in it.

Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R.

Methods in molecular biology (Clifton, N.J.)
Deep learning has emerged as a powerful tool for solving complex problems, including reconstruction of gene regulatory networks within the realm of biology. These networks consist of transcription factors and their associations with genes they regula...