AIMC Topic: Proteins

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BionoiNet: ligand-binding site classification with off-the-shelf deep neural network.

Bioinformatics (Oxford, England)
MOTIVATION: Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects...

DEEPrior: a deep learning tool for the prioritization of gene fusions.

Bioinformatics (Oxford, England)
SUMMARY: In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, ...

Protein docking model evaluation by 3D deep convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Many important cellular processes involve physical interactions of proteins. Therefore, determining protein quaternary structures provide critical insights for understanding molecular mechanisms of functions of the complexes. To complemen...

DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins.

Bioinformatics (Oxford, England)
MOTIVATION: The success of genome sequencing techniques has resulted in rapid explosion of protein sequences. Collections of multiple homologous sequences can provide critical information to the modeling of structure and function of unknown proteins....

Multi-scale structural analysis of proteins by deep semantic segmentation.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidat...

Protein-protein interaction site prediction through combining local and global features with deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions (PPIs) play important roles in many biological processes. Conventional biological experiments for identifying PPI sites are costly and time-consuming. Thus, many computational approaches have been proposed to ...

Graph embedding on biomedical networks: methods, applications and evaluations.

Bioinformatics (Oxford, England)
MOTIVATION: Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks ...

MUFold-SSW: a new web server for predicting protein secondary structures, torsion angles and turns.

Bioinformatics (Oxford, England)
MOTIVATION: Protein secondary structure and backbone torsion angle prediction can provide important information for predicting protein 3D structures and protein functions. Our new methods MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn,...

DeepMSPeptide: peptide detectability prediction using deep learning.

Bioinformatics (Oxford, England)
SUMMARY: The protein detection and quantification using high-throughput proteomic technologies is still challenging due to the stochastic nature of the peptide selection in the mass spectrometer, the difficulties in the statistical analysis of the re...