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 AI; 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
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 applied in several other application scenarios, which are outlined
in the paper.

该研究提出了一种结合机器学习和 Answer Set Programming 技术的新框架，以便于帮助医生和研究人员理解神经疾病的复杂机制，并在神经疾病演化模拟任务中发挥重要作用。