AIMC Topic: Protein Interaction Maps

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Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning.

Journal of clinical hypertension (Greenwich, Conn.)
Preeclampsia (PE) is a pregnancy-specific disorder characterized by an unclearly understood pathogenesis and poses a great threat to maternal and fetal safety. Cuproptosis, a novel form of cellular death, has been implicated in the advancement of var...

Diagnostic Power of MicroRNAs in Melanoma: Integrating Machine Learning for Enhanced Accuracy and Pathway Analysis.

Journal of cellular and molecular medicine
This study identifies microRNAs (miRNAs) with significant discriminatory power in distinguishing melanoma from nevus, notably hsa-miR-26a and hsa-miR-211, which have exhibited diagnostic potential with accuracy of 81% and 78% respectively. To enhance...

Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.

Journal of cellular and molecular medicine
Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes is crucial for understanding tumorigenesis, developing targeted therapies and identifying rational ...

Exploring the Mechanisms of Sanguinarine in the Treatment of Osteoporosis by Integrating Network Pharmacology Analysis and Deep Learning Technology.

Current computer-aided drug design
BACKGROUND: Sanguinarine (SAN) has been reported to have antioxidant, antiinflammatory, and antimicrobial activities with potential for the treatment of osteoporosis (OP).

New GO-based measures in multiple network alignment.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interaction (PPI) networks provide valuable insights into the function of biological systems. Aligning multiple PPI networks may expose relationships beyond those observable by pairwise comparisons. However, assessing the ...

Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network.

Briefings in bioinformatics
Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to en...

INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction.

Briefings in bioinformatics
An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constraints in time and cost of the associated 'wet lab' experiments. In silico PPI inference methods are ideal tools t...

Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.

Nucleic acids research
The intricacies of the human genome, manifested as a complex network of genes, transcend conventional representations in text or numerical matrices. The intricate gene-to-gene relationships inherent in this complexity find a more suitable depiction i...

DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.

Nucleic acids research
Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting t...