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 informationtheoretic 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 stateof-the-art methods.

• Type: Conference
• Year: 2007
• Where: BIRD
• Authors: Patrick Emmanuel Meyer, Kevin Kontos, Gianluca Bontempi
• Keywords: Artificial Microarray Datasets, Bioinformatics, BIRD 2007, Information-theoretic Network Inference, Reference Information-theoretic Methods