A Feature-Learning based method for the disease-gene prediction problem

We predict disease-genes relations on the human interactome network using a methodology that jointly learns functional and connectivity patterns surrounding proteins. Contrary to other data structures, the interactome is characterised by high incompleteness and absence of explicit negative knowledge, which makes predictive tasks particularly challenging. 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 the performance of RW2 and other compared state-of-the-art algorithms is extremely sensitive to the interactome used, and to the adopted disease categorisations, since this influences the ability to capture regularities in presence of sparsity and incompleteness.

A Feature-Learning based method for the disease-gene prediction problem / Madeddu, Lorenzo; Stilo, Giovanni; Velardi, Paola. - In: INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS. - ISSN 1748-5673. - 24:1(2020). [10.1504/IJDMB.2020.10031422]

Titolo: A Feature-Learning based method for the disease-gene prediction problem
Autori: 
MADEDDU, LORENZO
STILO, GIOVANNI
VELARDI, Paola
Data di pubblicazione: 2020
Rivista: 
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS  
Citazione: A Feature-Learning based method for the disease-gene prediction problem / Madeddu, Lorenzo; Stilo, Giovanni; Velardi, Paola. - In: INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS. - ISSN 1748-5673. - 24:1(2020). [10.1504/IJDMB.2020.10031422]
Handle: http://hdl.handle.net/11573/1435322
Appartiene alla tipologia:01a Articolo in rivista

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