The construction of Gene Regulatory Networks (GRNs) is one of the most important issues in systems biology. To infer a large-scale GRN with a nonlinear mathematical model, researchers need to encounter the time-consuming problem due to the large number of network parameters involved. In recent years, the cloud computing technique has been widely used to solve large-scale problems. Among others, Hadoop is currently the most well-known and reliable cloud computing framework, which allows users to analyze large amount of data in a distributed environment (i.e., MapReduce). It also supports data backup and data recovery mechanisms.
This study proposes an Island-based GAPSO algorithm under the Hadoop cloud computing environment to infer large-scale GRNs. GAPSO exploited the position and velocity functions of PSO, and integrated the operations of Genetic Algorithm. This approach is often used to derive the optimal solution in nonlinear mathematical models. Several sets of experiments have been conducted, in which the number of network nodes varied from 50 to 125. The experiments were executed in the Hadoop distributed environment with 10, 20, and 26 computers, respectively. In the experiments of inferring the network with 125 gene nodes on the largest Hadoop cluster (i.e. 26 computers), the proposed framework performed up to 9.7 times faster than the stand-alone computer. It means that our work can successfully reduce 90% of the computation time in a single experimental run.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0913112-052307 |
| Date | 13 September 2012 |
| Creators | Huang, Wei-Jhe |
| Contributors | Yuh-Jiuan Tsay, Wei-Po Lee, Te-min Chang, Bing-Chiang Jeng |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0913112-052307 |
| Rights | user_define, Copyright information available at source archive |
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