AIMC Topic: Protein Interaction Maps

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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...

Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson's Diseases and Improving the Disease Classification Using Support Vector Machine.

BioMed research international
BACKGROUND: Parkinson's disease (PD) is a neurological disorder that is marked by the deficit of neurons in the midbrain that changes motor and cognitive function. In the substantia nigra, the selective demise of dopamine-producing neurons was the ma...

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm.

BioMed research international
BACKGROUND: Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes.

Efficient link prediction in the protein-protein interaction network using topological information in a generative adversarial network machine learning model.

BMC bioinformatics
BACKGROUND: The investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the wet-lab, therefore, several in silico approaches have been implemented to narrow down the candidates...

GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning.

IEEE/ACM transactions on computational biology and bioinformatics
The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potentia...

The use of machine learning to discover regulatory networks controlling biological systems.

Molecular cell
Biological systems are composed of a vast web of multiscale molecular interactors and interactions. High-throughput technologies, both bulk and single cell, now allow for investigation of the properties and quantities of these interactors. Computatio...

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks.

PloS one
Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, ...

Computed structures of core eukaryotic protein complexes.

Science (New York, N.Y.)
Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coe...