A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data.

ABSTRACT: Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. It is of great significance to identify drug-induced target-organ toxicity, especially the detailed pathological findings, which are crucial for toxicity assessment, in the early stage of drug development process. A large variety of studies have devoted to identify drug toxicity. However, most of them are limited to single organ or only binary toxicity. Here we proposed a novel multi-label learning model named Att-RethinkNet, for predicting drug-induced pathological findings targeted on liver and kidney based on toxicogenomics data. The Att-RethinkNet is equipped with a memory structure and can effectively use the label association information. Besides, attention mechanism is emb

SUBMITTER: Su R 

PROVIDER: S-EPMC9451100 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature