A logic-based framework leveraging neural networks for studying the evolution of neurological disorders. (English) Zbl 1472.68187

Summary: Deductive formalisms have been strongly developed in recent years; among them, answer set programming (ASP) gained some momentum and has been lately fruitfully employed in many real-world scenarios. Nonetheless, in spite of a large number of success stories in relevant application areas, and even in industrial contexts, deductive reasoning cannot be considered the ultimate, comprehensive solution to artificial intelligence; indeed, in several contexts, other approaches result to be more useful. Typical bioinformatics tasks, for instance classification, are currently carried out mostly by machine learning (ML)-based solutions.
In this paper, we focus on the relatively new problem of analyzing the evolution of neurological disorders. In this context, ML approaches already demonstrated to be a viable solution for classification tasks; here, we show how ASP can play a relevant role in the brain evolution simulation task. In particular, we propose a general and extensible framework to support physicians


68T27 Logic in artificial intelligence
68N17 Logic programming
92-08 Computational methods for problems pertaining to biology
92-10 Mathematical modeling or simulation for problems pertaining to biology
92B20 Neural networks for/in biological studies, artificial life and related topics
92C20 Neural biology
92C50 Medical applications (general)
Full Text: DOI arXiv


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