Details

Autor(en) / Beteiligte

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

Titel

NES 2 RA: Network expansion by stratified variable subsetting and ranking aggregation

Ist Teil von

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

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 NES 2 RA, 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. NES 2 RA 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 NES 2 RA 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 NES 2 RA approach and we also present and validate expansions performed on the model plant Arabidopsis thaliana and the model bacteria Escherichia coli.