Details

Autor(en) / Beteiligte

• Asnicar Francesco
• Masera Luca
• Coller Emanuela
• Gallo Caterina
• Sella Nadir
• Tolio, Thomas
• Morettin Paolo
• Erculiani Luca
• Galante, Francesca
• Stanislau, Semeniuta
• Malacarne Giulia
• Engelen Kristof
• Argentini, Andrea
• Cavecchia Valter
• Moser, Claudio
• Blanzieri Enrico

Titel

NES2RA

Ist Teil von

• The international journal of high performance computing applications, 2018-05, Vol.32 (3), p.380-392

Ort / Verlag

London: SAGE PUBLICATIONS, INC

Erscheinungsjahr

2018

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Gene network expansion is a task of the foremost importance in computational biology. Gene network expansion aims at finding new genes to expand a given known gene network. To this end, we developed gene@home, a BOINC-based project that finds candidate genes that expand known local gene networks using NESRA. In this paper, we present NES2RA, a novel approach that extends and improves NESRA by modeling, using a probability vector, the confidence of the presence of the genes belonging to the local gene network. NES2RA adopts intensive variable-subsetting strategies, enabled by the computational power provided by gene@home volunteers. In particular, we use the skeleton procedure of the PC-algorithm to discover candidate causal relationships within each subset of variables. Finally, we use state-of-the-art aggregators to combine the results into a single ranked candidate genes list. The resulting ranking guides the discovery of unknown relations between genes and a priori known local gene networks. Our experimental results show that NES2RA outperforms the PC-algorithm and its order-independent PC-stable version, ARACNE, and our previous approach, NESRA. In this paper we extensively discuss the computational aspects of the NES2RA approach and we also present and validate expansions performed on the model plant Arabidopsis thaliana and the model bacteria Escherichia coli.