AIMC Topic: Gene Regulatory Networks

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Identification of Co-diagnostic Genes for Heart Failure and Hepatocellular Carcinoma Through WGCNA and Machine Learning Algorithms.

Molecular biotechnology
This research delves into the intricate relationship between hepatocellular carcinoma (HCC) and heart failure (HF) by exploring shared genetic characteristics and molecular processes. Employing advanced methodologies such as differential analysis, we...

Graph global attention network with memory: A deep learning approach for fake news detection.

Neural networks : the official journal of the International Neural Network Society
With the proliferation of social media, the detection of fake news has become a critical issue that poses a significant threat to society. The dissemination of fake information can lead to social harm and damage the credibility of information. To add...

Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation.

Nature cell biology
In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulato...

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks.

PLoS computational biology
We present the Fast Greedy Equivalence Search (FGES)-Merge, a new method for learning the structure of gene regulatory networks via merging locally learned Bayesian networks, based on the fast greedy equivalent search algorithm. The method is competi...

Identification of Potential Neddylation-related Key Genes in Ischemic Stroke based on Machine Learning Methods.

Molecular neurobiology
Ischemic stroke (IS) is a complex neurological disease that can lead to severe disability or even death. Understanding the molecular mechanisms involved in the occurrence and progression of IS is of great significance for developing effective treatme...

scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.

International journal of molecular sciences
Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designe...

Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning.

Hereditas
BACKGROUND: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in ...

Predicting transcriptional outcomes of novel multigene perturbations with GEARS.

Nature biotechnology
Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in...

Unraveling the Molecular Puzzle: Exploring Gene Networks across Diverse EMT Status of Cell Lines.

International journal of molecular sciences
Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in sing...