A Decision Support System for Reverse Engineering Gene Regulatory Networks

Daniele Peri, Salvatore Gaglio, Antonino Fiannaca, Massimo La Rosa

Risultato della ricerca: Other

Abstract

In this paper we present a knowledge-based system that aims at helping scientists in the reverse engineering process of gene regulatory networks. The main motivation of the proposed approach is to support scientists in the choice of the wide variety of algorithms and methods currently applied in the literature to infer Gene Regulatory Networks starting from gene expression measured using microarray technology. The Decision Support System (DSS) architecture is based on an ontology to model the knowledge base, a logical reasoner that builds the workflow of tasks to be done starting from the user’s request and a set of rules, and, finally, an agenda that runs the algorithms and software scheduled by the logical reasoner. The Decision Support System allows the interaction with the user in order to show intermediate results, refine the results and enrich the knowledge base with new expertise.
Lingua originaleEnglish
Pagine81-83
Numero di pagine3
Stato di pubblicazionePublished - 2009

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Reverse engineering
Decision support systems
Genes
Knowledge based systems
Microarrays
Gene expression
Ontology

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A Decision Support System for Reverse Engineering Gene Regulatory Networks. / Peri, Daniele; Gaglio, Salvatore; Fiannaca, Antonino; La Rosa, Massimo.

2009. 81-83.

Risultato della ricerca: Other

Peri, D, Gaglio, S, Fiannaca, A & La Rosa, M 2009, 'A Decision Support System for Reverse Engineering Gene Regulatory Networks', pagg. 81-83.
Peri D, Gaglio S, Fiannaca A, La Rosa M. A Decision Support System for Reverse Engineering Gene Regulatory Networks. 2009.
Peri, Daniele ; Gaglio, Salvatore ; Fiannaca, Antonino ; La Rosa, Massimo. / A Decision Support System for Reverse Engineering Gene Regulatory Networks. 3 pag.
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AB - In this paper we present a knowledge-based system that aims at helping scientists in the reverse engineering process of gene regulatory networks. The main motivation of the proposed approach is to support scientists in the choice of the wide variety of algorithms and methods currently applied in the literature to infer Gene Regulatory Networks starting from gene expression measured using microarray technology. The Decision Support System (DSS) architecture is based on an ontology to model the knowledge base, a logical reasoner that builds the workflow of tasks to be done starting from the user’s request and a set of rules, and, finally, an agenda that runs the algorithms and software scheduled by the logical reasoner. The Decision Support System allows the interaction with the user in order to show intermediate results, refine the results and enrich the knowledge base with new expertise.

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