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In this paper, we use the minimum description length (MDL) principle to reduce the rate of false positives for best-fit algorithms. The performance of these algorithms is evaluated <jats:italic>via<\/jats:italic> two metrics: the normalized-edge Hamming distance and the steady-state distribution distance. Results for synthetic networks and a well-studied budding-yeast cell cycle network show that MDL-based filtering is more effective than filtering based on conditional mutual information (CMI). In addition, MDL-based filtering provides better inference than the MDL algorithm itself.<\/jats:p>","DOI":"10.1186\/s13637-014-0013-2","type":"journal-article","created":{"date-parts":[[2014,7,2]],"date-time":"2014-07-02T17:22:58Z","timestamp":1404321778000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms"],"prefix":"10.1186","volume":"2014","author":[{"given":"Jie","family":"Fang","sequence":"first","affiliation":[]},{"given":"Hongjia","family":"Ouyang","sequence":"additional","affiliation":[]},{"given":"Liangzhong","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Edward R","family":"Dougherty","sequence":"additional","affiliation":[]},{"given":"Wenbin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,7,3]]},"reference":[{"key":"13_CR1","volume-title":"Genomic Signal Processing (Princeton Series in Applied Mathematics)","author":"I Shmulevich","year":"2007","unstructured":"I Shmulevich, ER Dougherty, Genomic Signal Processing (Princeton Series in Applied Mathematics) (Princeton University Press, Princeton, 2007)"},{"key":"13_CR2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898717631","volume-title":"Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks","author":"I Shmulevich","year":"2010","unstructured":"I Shmulevich, ER Dougherty, Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks (SIAM, Philadelphia, 2010)"},{"key":"13_CR3","volume-title":"REVEAL, a general reverse engineering algorithm for inference of genetic network architectures, in Pacific Symposium on Biocomputing","author":"S Liang","year":"1998","unstructured":"Liang S, Fuhrman S, Somogyi R: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures, in Pacific Symposium on Biocomputing. 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