Browsing by Author "Hsieh, Chih-Hung"

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An intelligent two-stage evolutionary algorithm for dynamic pathway identification from gene expression profiles
(2007-10-01)
Ho, Shinn-Ying
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Hsieh, Chih-Hung
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Yu, Fu-Chieh
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Huang, Hui-Ling
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生物資訊及系統生物研究所
From gene expression profiles, it is desirable to rebuild cellular dynamic regulation networks to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering, and pharmaceutics. The S-system model is suitable to characterize biochemical network systems and capable of analyzing the regulatory system dynamics. However, the inference of an S-system model of N-gene genetic networks has 2N(N+1) parameters in a set of nonlinear differential equations to be optimized. This paper proposes an intelligent two-stage evolutionary algorithm (iTEA) to efficiently infer the S-system models of genetic networks from time-series data of gene expression. To cope with the curse of dimensionality, the proposed algorithm consists of two stages, where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N+1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on an orthogonal experimental design (OED). At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm for handling noisy gene expression profiles. The effectiveness of iTEA is evaluated using simulated expression patterns with and without noise running on a single-processor PC. It is shown that 1) IGA is efficient enough to solve subproblems, 2) IGA is significantly superior to the existing method GA with simplex crossover (SPXGA), and 3) iTEA performs well in inferring S-system models for dynamic pathway identification.
Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis
(2006-09-01)
Ho, Shinn-Ying
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Hsieh, Chih-Hung
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Chen, Hung-Ming
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Huang, Hui-Ling
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生物科技學系
An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimized: maximal classification accuracy, minimal number of rules, and minimal number of used genes. An "intelligent" genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters. The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class) on average in terms of test classification accuracy (87.9%), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers. (c) 2006 Elsevier Ireland Ltd. All rights reserved.