Drug-Target Interaction Prediction Based on Adversarial Bayesian Personalized Ranking

Drug–target interaction prediction through domain-tuned network-based inference

MOTIVATION The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do...

Similarity based learning method for drug target interaction prediction

Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique fo...

BPR: Bayesian Personalized Ranking from Implicit Feedback

Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorization (MF) or adaptive knearest-neighbor (kNN). Even though these methods are des...

Optimizing drug–target interaction prediction based on random walk on heterogeneous networks

BACKGROUND Predicting novel drug-target associations is important not only for developing new drugs, but also for furthering biological knowledge by understanding how drugs work and their modes of action. As more data about drugs, targets, and their interactions becomes available, computational approaches have become an indispensible part of drug target association discovery. In this paper we a...