AIMC Topic: Protein Interaction Maps

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HNetGO: protein function prediction via heterogeneous network transformer.

Briefings in bioinformatics
Protein function annotation is one of the most important research topics for revealing the essence of life at molecular level in the post-genome era. Current research shows that integrating multisource data can effectively improve the performance of ...

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. Howe...

AFTGAN: prediction of multi-type PPI based on attention free transformer and graph attention network.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interaction (PPI) networks and transcriptional regulatory networks are critical in regulating cells and their signaling. A thorough understanding of PPIs can provide more insights into cellular physiology at normal and dis...

Perceiver CPI: a nested cross-attention network for compound-protein interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Compound-protein interaction (CPI) plays an essential role in drug discovery and is performed via expensive molecular docking simulations. Many artificial intelligence-based approaches have been proposed in this regard. Recently, two type...

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.

Methods in molecular biology (Clifton, N.J.)
Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have gre...

GNN-SubNet: disease subnetwork detection with explainable graph neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: The tremendous success of graphical neural networks (GNNs) already had a major impact on systems biology research. For example, GNNs are currently being used for drug target recognition in protein-drug interaction networks, as well as for...

An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph.

Briefings in bioinformatics
Identifying the potential compound-protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously imp...

SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning.

Molecular cancer research : MCR
UNLABELLED: Drug combination therapy has become a promising therapeutic strategy for cancer treatment. While high-throughput drug combination screening is effective for identifying synergistic drug combinations, measuring all possible combinations is...

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.

Briefings in bioinformatics
Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds i...

Deep graph representations embed network information for robust disease marker identification.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and prog...