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Protein Interaction Maps

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PLA-GNN: Computational inference of protein subcellular location alterations under drug treatments with deep graph neural networks.

Computers in biology and medicine
The aberrant protein sorting has been observed in many conditions, including complex diseases, drug treatments, and environmental stresses. It is important to systematically identify protein mis-localization events in a given condition. Experimental ...

Hierarchical graph learning for protein-protein interaction.

Nature communications
Protein-Protein Interactions (PPIs) are fundamental means of functions and signalings in biological systems. The massive growth in demand and cost associated with experimental PPI studies calls for computational tools for automated prediction and und...

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction.

Computers in biology and medicine
New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance o...

TripletProt: Deep Representation Learning of Proteins Based On Siamese Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Pretrained representations have recently gained attention in various machine learning applications. Nonetheless, the high computational costs associated with training these models have motivated alternative approaches for representation learning. Her...

Long-distance dependency combined multi-hop graph neural networks for protein-protein interactions prediction.

BMC bioinformatics
BACKGROUND: Protein-protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of...

A deep learning framework for identifying essential proteins based on multiple biological information.

BMC bioinformatics
BACKGROUND: Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein-protein interacti...

Gene Identification and Potential Drug Therapy for Drug-Resistant Melanoma with Bioinformatics and Deep Learning Technology.

Disease markers
BACKGROUND: Melanomas are skin malignant tumors that arise from melanocytes which are primarily treated with surgery, chemotherapy, targeted therapy, immunotherapy, radiation therapy, etc. Targeted therapy is a promising approach to treating advanced...

Empowering the discovery of novel target-disease associations via machine learning approaches in the open targets platform.

BMC bioinformatics
BACKGROUND: The Open Targets (OT) Platform integrates a wide range of data sources on target-disease associations to facilitate identification of potential therapeutic drug targets to treat human diseases. However, due to the complexity that targets ...

EPGAT: Gene Essentiality Prediction With Graph Attention Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Identifying essential genes and proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experimental constraints by exploring machine learning (ML) methods and the correla...

Gradient tree boosting and network propagation for the identification of pan-cancer survival networks.

STAR protocols
Cancer survival prediction is typically done with uninterpretable machine learning techniques, e.g., gradient tree boosting. Therefore, additional steps are needed to infer biological plausibility of the predictions. Here, we describe a protocol that...