DILIC: An AI-Based Classifier to Search for Drug-Induced Liver Injury Literature

Frontiers in Genetics

Namshik Han¹  Nicholas M. Katritsis²  Anika Liu³  Meabh MacMahon⁴  Sanjay Rathee⁵  Gehad Youssef⁵  Lilly Wollman⁵  Woochang Hwang⁵ 

[1] Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, United Kingdom;Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom;Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Cambridge, United Kingdom;LifeArc, Stevenage, United Kingdom;Milner Therapeutics Institute, University of Cambridge, Cambridge, United Kingdom;

关键词: drug-induced liver injury (DILI);    natural language processing (NLP);    machine learning (ML);    artificial intelligence (AI);    pattern mining;    shiny app;   

DOI  :  10.3389/fgene.2022.867946

来源: DOAJ

【 摘 要 】

Drug-induced liver injury (DILI) is a class of adverse drug reactions (ADR) that causes problems in both clinical and research settings. It is the most frequent cause of acute liver failure in the majority of Western countries and is a major cause of attrition of novel drug candidates. Manual trawling of the literature is the main route of deriving information on DILI from research studies. This makes it an inefficient process prone to human error. Therefore, an automatized AI model capable of retrieving DILI-related articles from the huge ocean of literature could be invaluable for the drug discovery community. In this study, we built an artificial intelligence (AI) model combining the power of natural language processing (NLP) and machine learning (ML) to address this problem. This model uses NLP to filter out meaningless text (e.g., stop words) and uses customized functions to extract relevant keywords such as singleton, pair, and triplet. These keywords are processed by an apriori pattern mining algorithm to extract relevant patterns which are used to estimate initial weightings for a ML classifier. Along with pattern importance and frequency, an FDA-approved drug list mentioning DILI adds extra confidence in classification. The combined power of these methods builds a DILI classifier (DILIC), with 94.91% cross-validation and 94.14% external validation accuracy. To make DILIC as accessible as possible, including to researchers without coding experience, an R Shiny app capable of classifying single or multiple entries for DILI is developed to enhance ease of user experience and made available at https://researchmind.co.uk/diliclassifier/. Additionally, a GitHub link (https://github.com/sanjaysinghrathi/DILI-Classifier) for app source code and ISMB extended video talk (https://www.youtube.com/watch?v=j305yIVi_f8) are available as supplementary materials.

【 授权许可】

Unknown