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EURASIP Journal on Bioinformatics and Systems Biology

, 2007:79879

Information Theoretic Methods for Bioinformatics


The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance-minimum redundancy MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

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Author: Patrick E Meyer - Kevin Kontos - Frederic Lafitte - Gianluca Bontempi


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