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.
Towards a Logic Programming Tool for Cancer Data Analysis
Dovier A.;Policriti A.
2020-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
