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

TL;DR

This paper presents a hybrid framework combining Machine Learning and Answer Set Programming to simulate and analyze the evolution of neurological disorders, aiding researchers and physicians in understanding complex brain mechanisms.

Contribution

It introduces a novel, extensible approach integrating ML and ASP for brain evolution simulation, addressing limitations of purely ML-based methods.

Findings

Demonstrates the framework's effectiveness in simulating neurological disorder evolution.

Shows how ASP complements ML in complex biological modeling.

Framework is adaptable to various biomedical applications.

Abstract

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.