Our group has recently developed gene@home, a BOINC project that permits to search for candidate genes for the expansion of a gene regulatory network using gene expression data. The gene@home project adopts intensive variablesubsetting strategies enabled by the computational power provided by the volunteers who have joined the project by means of the BOINC client, and exploits the PC algorithm for discovering putative causal relationships within each subset of variables. This paper presents our TN-Grid infrastructure that is hosting the gene@home project. Gene@home implements a novel method for Network Expansion by Subsetting and Ranking Aggregation (NESRA), producing a list of genes that are candidates for the gene network expansion task. NESRA is an algorithm that has: 1) a ranking procedure that systematically subsets the variables; the subsetting is iterated several times and a ranked list of candidates is produced by counting the number of times a relationship is found; 2) several ranking steps are executed with different values of the dimension of the subsets and with different number of iterations producing several ranked lists; 3) the ranked lists are aggregated by using a state-of-the-art ranking aggregator. In our experimental results, we show that a single ranking step is enough to outperform both PC and PC*. Evaluations and experiments are done by means of the gene@home project on a real gene regulatory network of the model plant Arabidopsis thaliana.

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-ofthe-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.

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