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
Sebastiano Vascon
;Rocco Tripodi
;Valentini, Giorgio
;Marcello Pelillo
2018-01-01
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.File | Dimensione | Formato | |
---|---|---|---|
prl_protein.pdf
non disponibili
Tipologia:
Versione dell'editore
Licenza:
Accesso chiuso-personale
Dimensione
970.62 kB
Formato
Adobe PDF
|
970.62 kB | Adobe PDF | Visualizza/Apri |
_to_submit__PRL_Protein_Function_Prediction.pdf
Open Access dal 06/04/2020
Descrizione: Post-Print
Tipologia:
Documento in Post-print
Licenza:
Creative commons
Dimensione
512.12 kB
Formato
Adobe PDF
|
512.12 kB | Adobe PDF | Visualizza/Apri |
I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.