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| Identifying E.coli Gene Regulatory Network Based on HilbertSchmidt Independence Criterion |
| College of Life Information Science & Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China |
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Abstract The key of genetic system modeling is to identify the causal relationships of the genes. In the third Dialogue for Reverse Engineering Assessments and Methods（DREAM3）competition, E.coli dataset was generated with a ‘true’ biological gene networks. The aim of this work is to recover gene network structure from the data. Here we presented a statistical independent measurement method based on reproducing kernel Hilbert space (RKHS) - HilbertSchmidt independence criteria (HSIC). Different from others, which either use the classification rate, or parameterized methods，the proposed measurement is a nonparametric direct measurement with independence. Comparative experiment results showed that the method was efficient in recovering the regulatory relationships between genes even with small data sample. Specifically, the HSIC achieved a better result than the classical Granger Causality (GC) method as well as the differential equations based method, which was the best in DREAM3 contest. The AUROC values obtained by HSIC is 23 percent higher than GC method, and 3.9 percent higher than the best performer of this contest. In addition, the computational efficiency of HSIC method was 3 orders higher than differential equations based method.
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