Logical analysis of numerical data
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Error-free and best-fit extensions of partially defined Boolean functions
Information and Computation
On Learning Gene Regulatory Networks Under the Boolean Network Model
Machine Learning
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Boolean network is one of the commonly used methods for building gene regulatory networks from time series microarray data. However, it has a major drawback that requires heavy computing times to infer large scale gene networks. This paper proposes a variable selection method to reduce Boolean network computing times using the chi-square statistics for testing independence in two way contingency tables. We compare the computing times and the accuracy of the estimated network structure by the proposed method with those of the original Boolean network method. For the comparative studies, we use simulated data and a real yeast cell-cycle gene expression data (Spellman et al., 1998). The comparative results show that the proposed variable selection method improves the computing time of Boolean network algorithm. We expect the proposed variable selection method to be more efficient for the large scale gene regulatory network studies.