Abstract
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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Meyer, P.E., Kontos, K., Bontempi, G. (2007). Biological Network Inference Using Redundancy Analysis. In: Hochreiter, S., Wagner, R. (eds) Bioinformatics Research and Development. BIRD 2007. Lecture Notes in Computer Science(), vol 4414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71233-6_2
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DOI: https://doi.org/10.1007/978-3-540-71233-6_2
Publisher Name: Springer, Berlin, Heidelberg
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