Applying data mining to learn system dynamics in a biological model |
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| Affiliation: | 1. Department of Information Management, National Sun Yat-sen University, 70 Lien-hai Road, Kaohsiung City 804, Taiwan, ROC;2. Department of Information Management, National Sun Yat-sen University, Taiwan;1. UM-DAE Centre for Excellence in Basic Sciences, Vidyanagari, Santacruz (E), Mumbai 400098, India;2. University Department of Biotechnology, University of Mumbai, Vidyanagari, Santacruz (East), Mumbai 400098, India;1. School of Nuclear Science and Technology and Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026, China;2. Plasma Physics Laboratory, Princeton University, Princeton, NJ 08543, USA;3. School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui 230026, China;1. School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, Jiangsu, China;2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, Jiangsu, China |
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| Abstract: | Data mining consists of a set of powerful methods that have been successfully applied to many different application domains, including business, engineering, and bioinformatics. In this paper, we propose an innovative approach that uses genetic algorithms to mine a set of temporal behavior data output by a biological system in order to determine the kinetic parameters of the system. Analyzing the behavior of a biological network is a complicated task. In our approach, the machine learning method is integrated with the framework of system dynamics so that its findings are expressed in a form of system dynamics model. An application of the method to the cell division cycle model has shown that the method can discover approximate parametric values of the system and reproduce the input behavior. |
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