Manifold regularized matrix factorization for drug-drug interaction prediction.

Drug-drug interaction (DDI) prediction is one of the most important tasks in drug discovery. Prediction of potential DDIs helps to reduce unexpected side effects in the lifecycle of drugs, and is important for the drug safety surveillance. Here, we formulate the drug-drug interaction prediction as a matrix completion task, and project drugs in the interaction space into a low-dimensional space.

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance.

A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs.

Background: Predicting piwi-interacting RNA (piRNA) is an important topic in the small non-coding RNAs, which provides clues for understanding the generation mechanism of gamete. To the best of our knowledge, several machine learning approaches have been proposed for the piRNA prediction, but there is still room for improvements.

Results: In this paper, we develop a genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs.

Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features.

Background: Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete.

Methods: In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods.