- 一般社団法人 人工知能学会
- 人工知能学会論文誌 (ISSN:13460714)
- vol.34, no.2, pp.D-I81_1-18, 2019-03-01 (Released:2019-03-01)
In drug development, Drug-Induced Liver Injury (DILI) is a significant cause of discontinuation of development, and safety evaluation and management technology at early development stage are highly required. In recent years, toxicity prediction by in silico analysis is expected, and the machine learning research using omics data has attracted attention. However, the lack of explanation of machine learning is a problem. In order to make an appropriate safety assessment, it is necessary to clarify the mechanism of the toxicity (toxic course). In this study, we focus on the toxic course and propose an ontological model of the liver toxicity, which systematizes toxicity knowledge based on a consistent viewpoint. In application research, we introduce a prototype of a knowledge system for supporting toxicity mechanism interpretation. Based on the ontology, this system provides information flexibly according to the user's purpose by using semantic technologies. The system provides a graph visualization function in which nodes correspond to concepts and edges correspond to interactions between concepts. In such a visualization function, a toxic course map shows causal relationships of the toxic process. We illustrate examples of application to safety assessment and management by combining ontological and data-driven methodologies. Our ontological engineering solution contributes to converting from data to higher-order knowledge and making the data explainable in both human and computer understandable manner. We believe that our approach can be expected as a fundamental technology and will be useful for a wide range of applications in interdisciplinary areas.