NES2RA: Network expansion by stratified variable subsetting and ranking aggregation. (May 2018)

• Record Type: Journal Article
• 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
• Other Names: Vega-Rodríguez Miguel A guest-editor.; Rubio-Largo Álvaro guest-editor.
• Is Part Of: International journal of high performance computing applications. Volume 32:Number 3(2018)
• Page Start: 380
• Page End: 392
• Publication Date: 2018-05
• Subjects: Volunteer computing -- distributed computing -- BOINC -- bioinformatics -- gene network expansion; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342016662508
• Languages: English
• ISSNs: 1094-3420

Abstract

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 theskeleton 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 .