Biological Network Inference Using Redundancy Analysis

The paper presents MRNet, an original method for inferring genetic networks from microarray data. This method is based on Maximum Relevance - Minimum Redundancy (MRMR), an effective information-theoretic technique for feature selection. MRNet is compared experimentally to Relevance Networks (RelNet) and ARACNE, two state-of-the-art information-theoretic network inference methods, on several artificial microarray datasets. The results show that MRNet is competitive with the reference information-theoretic methods on all datasets. In particular, when the assessment criterion attributes a higher weight to precision than to recall, MRNet outperforms the state-of-the-art methods.

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