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|Title:||Counting motifs in the entire biological network from noisy and incomplete data (extended abstract)||Authors:||Tran, N.H.
|Issue Date:||2013||Citation:||Tran, N.H.,Choi, K.P.,Zhang, L. (2013). Counting motifs in the entire biological network from noisy and incomplete data (extended abstract). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7821 LNBI : 269-270. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-37195-0_24||Abstract:||Small over-represented motifs in biological networks are believed to represent essential functional units of biological processes. A natural question is to gauge whether a motif occurs abundantly or rarely in a biological network. Given that high-throughput biotechnology is only able to interrogate a portion of the entire biological network with non-negligible errors, we develop a powerful method to correct link errors in estimating undirected or directed motif counts in the entire network from noisy subnetwork data. © 2013 Springer-Verlag.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/104554||ISBN:||9783642371943||ISSN:||03029743||DOI:||10.1007/978-3-642-37195-0_24|
|Appears in Collections:||Staff Publications|
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