A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders

摘要

Deductive formalisms have been strongly developed in recent years; among them, answer setprogramming (ASP) gained some momentum and has been lately fruitfully employed in manyreal-world scenarios. Nonetheless, in spite of a large number of success stories in relevant applicationareas, and even in industrial contexts, deductive reasoning cannot be considered theultimate, comprehensive solution to artificial intelligence; indeed, in several contexts, other approachesresult to be more useful. Typical bioinformatics tasks, for instance classification, arecurrently carried out mostly by machine learning (ML)-based solutions.In this paper, we focus on the relatively new problem of analyzing the evolution of neurologicaldisorders. In this context, ML approaches already demonstrated to be a viable solution forclassification tasks; here, we show how ASP can play a relevant role in the brain evolution simulationtask. In particular, we propose a general and extensible framework to support physicians and researchers at understanding the complex mechanisms underlying neurological disorders.The framework relies on a combined use of ML and ASP, and is general enough to be appliedin several other application scenarios, which are outlined in the paper.