An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).

ABSTRACT: The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) ( http://www.gutrhythm.com/public_database ) is a new source of electrophysiological big data for drug research. Over the past 2 years, the database has doubled in size, and now contains the electrophysiological profiles of 172 drugs across 11,943 datasets. This study used a state-of-the-art deep-learning model with time-series classification to explore the feasibility of using raw electrophysiological recordings from tissues to predict ADRs. The GIPADD contains the recordings of the electrical activity of various gastrointestinal tissues (stomach, duodenum, ileum, and colon) exposed to a drug at three or mor

SUBMITTER: Chau HC 

PROVIDER: S-EPMC12003867 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature