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Protein Interaction Maps

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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...

Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.

Briefings in bioinformatics
New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. H...

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach.

Bioscience reports
BACKGROUND: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patien...

Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples.

Bioengineered
Osteoporosis is a progressive bone disease in the elderly and lacks an effective classification method of patients. This study constructed a gene signature for an accurate prediction and classification of osteoporosis patients. Three gene expression ...