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

• Published in: The international journal of high performance computing applications 2018-05, Vol.32 (3), p.380-392
• Main Authors: 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
• Format: Article
• Language: English
• ISSN: 1094-3420; 1741-2846
• DOI: 10.1177/1094342016662508

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.