Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Learning Multi-Time Delay Gene Network Using Bayesian Network Framework
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Modeling gene-regulatory networks using evolutionary algorithms and distributed computing
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid - Volume 01
Learning Gene Network Using Conditional Dependence
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
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The major challenge of inferring genetic network is mining the dependencies and regulating relationship among genes. The paper tries to address this problem using Genetic Algorithms to infer the transcription regulatory network. While Genetic Algorithms(GA) are able to infer smaller networks with good sensitivity and precision, several generations and much greater computation power are required to infer regulatory networks from realistic data. Here a modified GA that uses statistical techniques to narrow the search space is proposed. The system is tested on the publicly available datasets of the Hela cell cycle and Yeast cell cycle. The results have been compared with regulatory networks inferred by using second order differential equations. It is found that the sensitivity and specificity are at par with differential equation method and has a considerable improvement in comparison with the Basic GA method.