[HTML][HTML] Protein–protein interaction prediction with deep learning: A comprehensive review
Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …
molecules. Thus, one may obtain biological insights into protein functions, disease …
Machine learning solutions for predicting protein–protein interactions
Proteins are “social molecules.” Recent experimental evidence supports the notion that
large protein aggregates, known as biomolecular condensates, affect structurally and …
large protein aggregates, known as biomolecular condensates, affect structurally and …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high
cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the …
cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the …
Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier
Protein-protein interactions (PPIs) are involved with most cellular activities at the proteomic
level, making the study of PPIs necessary to comprehending any biological process …
level, making the study of PPIs necessary to comprehending any biological process …
Multifaceted protein–protein interaction prediction based on Siamese residual RCNN
Motivation Sequence-based protein–protein interaction (PPI) prediction represents a
fundamental computational biology problem. To address this problem, extensive research …
fundamental computational biology problem. To address this problem, extensive research …
Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Abstract Background Protein-protein interactions (PPIs) are critical for many biological
processes. It is therefore important to develop accurate high-throughput methods for …
processes. It is therefore important to develop accurate high-throughput methods for …
Graph-based prediction of protein-protein interactions with attributed signed graph embedding
Abstract Background Protein-protein interactions (PPIs) are central to many biological
processes. Considering that the experimental methods for identifying PPIs are time …
processes. Considering that the experimental methods for identifying PPIs are time …
Improving random forest predictions in small datasets from two-phase sampling designs
Background While random forests are one of the most successful machine learning
methods, it is necessary to optimize their performance for use with datasets resulting from a …
methods, it is necessary to optimize their performance for use with datasets resulting from a …
Deep neural network based predictions of protein interactions using primary sequences
H Li, XJ Gong, H Yu, C Zhou - Molecules, 2018 - mdpi.com
Machine learning based predictions of protein–protein interactions (PPIs) could provide
valuable insights into protein functions, disease occurrence, and therapy design on a large …
valuable insights into protein functions, disease occurrence, and therapy design on a large …