In this paper we propose an extended version of random walks, named Random Watcher-Walker (RW2), to predict disease-genes relations on the Human Interactome network. $RW^2$ is able to learn rich representations of disease genes (or gene products) features by jointly considering functional and connectivity patterns surrounding proteins. Our method successfully compares with the best-known system for disease gene prediction and other state-of-the-art graph-based methods. We perform sensitivity analysis and apply perturbations to ensure robustness. Differently from previous studies, our results demonstrate that connectivity alone is not sufficient to classify disease-related genes.

Predicting disease genes for complex diseases using random watcher-walker / MADEDDU, LORENZO; STILO, GIOVANNI; VELARDI, Paola. - (2020), pp. 458-465. ((Intervento presentato al convegno 35th Annual ACM Symposium on Applied Computing, SAC 2020 tenutosi a Brno; Czech Republic [10.1145/3341105.3373979].

Predicting disease genes for complex diseases using random watcher-walker

Lorenzo Madeddu;Giovanni Stilo;Paola Velardi
2020

Abstract

In this paper we propose an extended version of random walks, named Random Watcher-Walker (RW2), to predict disease-genes relations on the Human Interactome network. $RW^2$ is able to learn rich representations of disease genes (or gene products) features by jointly considering functional and connectivity patterns surrounding proteins. Our method successfully compares with the best-known system for disease gene prediction and other state-of-the-art graph-based methods. We perform sensitivity analysis and apply perturbations to ensure robustness. Differently from previous studies, our results demonstrate that connectivity alone is not sufficient to classify disease-related genes.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1360406
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