An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target Interactions.

Background: In recent years, analyzing complex biological networks to predict future links in such networks has attracted the attention of many medical and computer science researchers. The discovery of new drugs is one of the application cases for predicting future connections in biological networks. The operation of drug-target interactions prediction (DTIP) can be considered a fundamental step in identifying potential interactions between drug and target to identify new drugs.

DTIP-TC2A: An analytical framework for drug-target interactions prediction methods.

Identifying drug-target interactions through computational methods is raised an important and key step in the process of drug discovery and drug-oriented research during the last years. In addition to the advantages of existing computational methods, there are also challenges that affect methods' efficiency and provide obstacles in the direction of developing these computational methods. However, the literature suffers from lacking a comprehensive and comparative analysis concerning drug-target interactions prediction (DTIP) focusing on the analysis of technical and challenging aspects.

EPTs-TL: A two-level approach for efficient event prediction in healthcare.

Recently, the event prediction on time series (EPTs) was discussed as one of the important and interesting research trends that its usage is growing for taking proper decisions in the various sciences. In the real-world, time series event-based analysis can pose as one of the challenging prediction problems in healthcare, which have a direct impact and a key role in supporting health management. In this paper, an efficient approach of two-level (TL) is proposed to the EPTs problem in healthcare, which named EPTs-TL.