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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Wu, Shinq-Jen Wu, Cheng-Tao |
| Description | Author Affiliation: Wu SJ ( Department of Electrical Engineering, Da-Yeh University, Chang-Hwa, Taiwan, ROC. Electronic address: jen@mail.dyu.edu.tw.); Wu CT ( Department of Electrical Engineering, Da-Yeh University, Chang-Hwa, Taiwan, ROC.) |
| Abstract | A large challenge in the post-genomic era is to obtain the quantitatively dynamic interactive information of the important constitutes of underlying systems. The S-system is a dynamic and structurally rich model that determines the net strength of interactions between genes and/or proteins. Good generation characteristics without the need for prior information have allowed S-systems to become one of the most promising canonical models. Various evolutionary computation technologies have recently been developed for the identification of system parameters and skeletal-network structures. However, the gaps between the truncated and preserved terms remain too small. Additionally, current research methods fail to identify the structures of high dimensional systems (e.g., 30 genes with 1800 connections). Optimization technologies should converge fast and have the ability to adaptively adjust the search. In this study, we propose a seeding-inspired chemotaxis genetic algorithm (SCGA) that can force evolution to adjust the population movement to identify a favorable location. The seeding-inspired training strategy is a method to achieve optimal results with limited resources. SCGA introduces seeding-inspired genetic operations to allow a population to possess competitive power (exploitation and exploration) and a winner-chemotaxis-induced population migration to force a population to repeatedly tumble away from an attractor and swim toward another attractor. SCGA was tested on several canonical biological systems. SCGA not only learned the correct structure within only one to three pruning steps but also ensures pruning safety. The values of the truncated terms were all smaller than 10 , even for a thirty-gene system. |
| File Format | HTM / HTML |
| ISSN | 14769271 |
| Journal | Computational Biology and Chemistry |
| Volume Number | 53PB |
| e-ISSN | 1476928X |
| Language | English |
| Publisher | Elsevier |
| Publisher Date | 2014-09-18 |
| Publisher Place | Great Britain (UK) |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Discipline Medical Informatics Discipline Chemistry Discipline Computational Biology |
| Content Type | Text |
| Resource Type | Article |
| Subject | Organic Chemistry Structural Biology Biochemistry Computational Mathematics |
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