Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-37195-0_24
Title: Counting motifs in the entire biological network from noisy and incomplete data (extended abstract)
Authors: Tran, N.H.
Choi, K.P. 
Zhang, L. 
Issue Date: 2013
Source: 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/10635/53282
ISBN: 9783642371943
ISSN: 03029743
DOI: 10.1007/978-3-642-37195-0_24
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