We predict disease-genes relations on the human interactome network using a methodology that jointly learns functional and connectivity patterns surrounding proteins. To exploit at best latent information in the network, we propose an extended version of random walks, named Random Watcher-Walker (RW2), which is shown to perform better than other state-of-the-art algorithms. We also show that performance ofRW2and other compared state-of-the-art algorithms is extremely sensitive to the interactome used, and to the adopted disease categorizations, since this influences the ability to capture regularities in presence of sparsity and incompleteness.

Predicting disease genes using connectivity and functional features / MADEDDU, LORENZO; STILO, GIOVANNI; VELARDI, Paola. - (2019). ((Intervento presentato al convegno 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019) tenutosi a San Diego; CA, USA.

Predicting disease genes using connectivity and functional features

MADEDDU, LORENZO
Investigation
;
Giovanni Stilo
Conceptualization
;
Paola Velardi
Conceptualization
2019

Abstract

We predict disease-genes relations on the human interactome network using a methodology that jointly learns functional and connectivity patterns surrounding proteins. To exploit at best latent information in the network, we propose an extended version of random walks, named Random Watcher-Walker (RW2), which is shown to perform better than other state-of-the-art algorithms. We also show that performance ofRW2and other compared state-of-the-art algorithms is extremely sensitive to the interactome used, and to the adopted disease categorizations, since this influences the ability to capture regularities in presence of sparsity and incompleteness.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1332609
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