AIMC Topic: Protein Interaction Maps

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An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-protein interactions (PPIs) are essential to understanding cellular mechanisms, signaling networks, disease processes, and drug development, as they represent the physical contacts and functional associations between proteins. Recent advances...

RGCNPPIS: A Residual Graph Convolutional Network for Protein-Protein Interaction Site Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of protein-protein interaction (PPI) sites is crucial for understanding the mechanisms of biological processes, developing PPI networks, and detecting protein functions. Currently, most computational methods primarily concentr...

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
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 approaches for predicting craniofacial anomalies with graph neural networks.

Computational biology and chemistry
This study explores the use of machine learning algorithms, including traditional approaches and graph neural networks (GNNs), to predict certain diseases by analyzing protein-protein interactions. Protein-protein interactions (PPIs) are complex, mul...

Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning.

Hereditas
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.

Leveraging Bioinformatics and Machine Learning for Identifying Prognostic Biomarkers and Predicting Clinical Outcomes in Lung Adenocarcinoma.

Genes
There exist significant challenges for lung adenocarcinoma (LUAD) due to its poor prognosis and limited treatment options, particularly in the advanced stages. It is crucial to identify genetic biomarkers for improving outcome predictions and guidin...

Identification and validation of biomarkers related to mitochondria during ex vivo lung perfusion for lung transplants based on machine learning algorithm.

Gene
BACKGROUND: Ex vivo lung perfusion (EVLP) is a critical strategy to rehabilitate marginal donor lungs, thereby increasing lung transplantation (LTx) rates. Ischemia-reperfusion (I/R) injury inevitably occurs during LTx. Exploring the common mechanism...

An experimental analysis of graph representation learning for Gene Ontology based protein function prediction.

PeerJ
Understanding protein function is crucial for deciphering biological systems and facilitating various biomedical applications. Computational methods for predicting Gene Ontology functions of proteins emerged in the 2000s to bridge the gap between the...

Protein-Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools.

Journal of proteome research
The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to si...