Knowledge-based identification of multicomponent therapies

Francesca Vitali, Francesca Mulas, Pietro Marini, Riccardo Bellazzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In recent years, several approaches have been proposed to improve the capacity of pharmaceutical research to support personalized care. An approach that takes advantages of the large amount of biological knowledge continuously collected in different repositories could improve the drug discovery process. In this context, networks are increasingly used as universal platforms to integrate the knowledge available on a complex disease. The objective of this work is to provide a knowledge-based strategy to support polypharmacology, a new promising approach for drug discovery. Given a specific disease, the proposed method is able to identify the possible targets by analysing the topological features of the related network. The network-based analysis defines a score aimed at ranking the targets and selecting their best combinations. The results obtained on Type 2 Diabetes Mellitus highlight the ability of the method to retrieve novel target candidates related to the considered disease.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Medicine - 14th Conference on Artificial Intelligence in Medicine, AIME 2013, Proceedings
Number of pages5
ISBN (Print)9783642383250
StatePublished - 2013
Externally publishedYes
Event14th Conference on Artificial Intelligence in Medicine, AIME 2013 - Murcia, Spain
Duration: May 29 2013Jun 1 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7885 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Conference on Artificial Intelligence in Medicine, AIME 2013


  • drug discovery
  • network-based bioinformatics
  • polypharmacology
  • target ranking

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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