Discovering Candidates for Gene Network Expansion by Distributed Volunteer Computing

被引：2

作者：

Asnicar, Francesco ^{[1
]}

Erculiani, Luca ^{[1
]}

Galante, Francesca ^{[1
]}

Gallo, Caterina ^{[1
]}

Masera, Luca ^{[1
]}

Morettin, Paolo ^{[1
]}

Sella, Nadir ^{[1
]}

Semeniuta, Stanislau ^{[1
]}

Tolio, Thomas ^{[1
]}

Malacarne, Giulia ^{[2
]}

Engelen, Kristof ^{[2
]}

Argentini, Andrea ^{[3
,4
]}

Cavecchia, Valter ^{[5
]}

Moser, Claudio ^{[2
]}

Blanzieri, Enrico ^{[1
]}

机构：

[1] Univ Trento, DISI, Via Sommar 9, Povo, Trento, Italy

[2] Fdn Edmund Mach, CRI, San Michele All Adige, Italy

[3] Univ Ghent, Dept Biochem, Ghent, Belgium

[4] Dept Med Prot Res VIB, Ghent, Belgium

[5] CNR IMEM, Trento, Italy

来源：

2015 IEEE TRUSTCOM/BIGDATASE/ISPA, VOL 3 | 2015年

关键词：

Volunteer Computing; Distributed Computing; BOINC; Bioinformatics; Gene Network Expansion; REGULATORY NETWORKS; EXPRESSION; ROBUST; ALGORITHM; PROTEINS;

D O I：

10.1109/Trustcom.2015.640

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 variable-subsetting strategies enabled by the computational power provided by the volunteers who have joined the project by means of the BOINC client. Our project exploits the PC algorithm (Spirtes and Glymour, 1991) in an iterative way, for discovering putative causal relationships within each subset of variables. This paper presents our infrastructure, called TN-Grid, 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 NESRA outperforms both the PC algorithm and its order-independent version called 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.

引用

页码：248 / 253

页数：6

共 36 条

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