An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features.

Background: Prediction of novel Drug-Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target.

Predicting Self-Interacting Proteins Using a Recurrent Neural Network and Protein Evolutionary Information.

Self-interacting proteins (SIPs) play crucial roles in biological activities of organisms. Many high-throughput methods can be used to identify SIPs. However, these methods are both time-consuming and expensive.

An Efficient Feature Extraction Technique Based on Local Coding PSSM and Multifeatures Fusion for Predicting Protein-Protein Interactions.

Background: Increasing evidence has indicated that protein-protein interactions (PPIs) play important roles in various aspects of the structural and functional organization of a cell. Thus, continuing to uncover potential PPIs is an important topic in the biomedical domain. Although various feature extraction methods with machine learning approaches have enhanced the prediction of PPIs.