Motivated by the observation that network-based methods for the automatic prediction of protein functions can greatly benefit from exploiting both the similarity between proteins and the similarity between functional classes (as encoded, e.g., in the Gene Ontology), in this paper we propose a novel approach to the problem, based on the notion of a “graph transduction game.” We envisage a (non-cooperative) game, played over a graph, where the players (graph vertices) represent proteins, the functional classes correspond to the (pure) strategies, and protein- and function-level similarities are combined into a suitable payoff function. Within this formulation, Nash equilibria turn out to provide consistent functional labelings of proteins, and we use classical replicator dynamics from evolutionary game theory to find them. To test the effectiveness of our approach we conducted experiments on five different organisms and three ontologies, and the results obtained show that our method compares favorably with state-of-the-art algorithms.

Protein function prediction as a graph-transduction game / S. Vascon, M. Frasca, R. Tripodi, G. Valentini, M. Pelillo. - In: PATTERN RECOGNITION LETTERS. - ISSN 0167-8655. - (2018). [Epub ahead of print]

Protein function prediction as a graph-transduction game

M. Frasca
;
G. Valentini
;
2018

Abstract

Motivated by the observation that network-based methods for the automatic prediction of protein functions can greatly benefit from exploiting both the similarity between proteins and the similarity between functional classes (as encoded, e.g., in the Gene Ontology), in this paper we propose a novel approach to the problem, based on the notion of a “graph transduction game.” We envisage a (non-cooperative) game, played over a graph, where the players (graph vertices) represent proteins, the functional classes correspond to the (pure) strategies, and protein- and function-level similarities are combined into a suitable payoff function. Within this formulation, Nash equilibria turn out to provide consistent functional labelings of proteins, and we use classical replicator dynamics from evolutionary game theory to find them. To test the effectiveness of our approach we conducted experiments on five different organisms and three ontologies, and the results obtained show that our method compares favorably with state-of-the-art algorithms.
Software; Signal Processing; 1707; Artificial Intelligence
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
2018
Article (author)
File in questo prodotto:
File Dimensione Formato  
PRL2017_GTG_APFP.pdf

accesso aperto

Tipologia: Pre-print (manoscritto inviato all'editore)
Dimensione 732.17 kB
Formato Adobe PDF
732.17 kB Adobe PDF Visualizza/Apri
1-s2.0-S0167865518301223-main (1).pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 949.58 kB
Formato Adobe PDF
949.58 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/619280
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 4
social impact