Towards a Logic Programming Tool for Cancer Data Analysis

Issue title: Special issue devoted to the best paper presented at the 32nd Italian Conference on Computational Logic (CILC 2017)

Guest editors: Dario Della Monica, Aniello Murano and Luigi Sauro

Article type: Research Article

Authors: Tarzariol, Alice^{; *} | Zanazzo, Eugenia | Dovier, Agostino | Policriti, Alberto

Affiliations: Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Università degli Studi di Udine, Italy. alice.tarzariol@aau.at, eugenia.zanazzo@gmail.com, agostino.dovier@uniud.it, alberto.policriti@uniud.it

Correspondence: [*] Also works: Department of Applied Informatics, Alpen-Adria-Universität Klagenfurt, Austria. Address for correspondence: Via delle Scienze 206, 33100, Udine, Italy

Abstract: The main goal of this work is to propose a tool-chain capable of analyzing a data collection of temporally qualified (genetic) mutation profiles, i.e., a collection of DNA-sequences (genes) that present variations with respect to their “healthy” versions. We implemented a system consisting of a front-end, a reasoning core, and a post-processor: the first transforms the input data retrieved from medical databases into a set of logical facts, while the last displays the computation results as graphs. Concerning the reasoning core, we employed the Answer Set Programming paradigm, which is capable of deducing complex information from data. However, since the system is modular, this component can be replaced by any logic programming tool for different kinds of data analysis. Indeed, we tested the use of a probabilistic inductive logic programming core.

Keywords: Bioinformatics, Big Data Analysis, ASP

DOI: 10.3233/FI-2020-1975

Journal: Fundamenta Informaticae, vol. 176, no. 3-4, pp. 299-319, 2020