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Counting Motifs in the Entire Biological Network from Noisy and Incomplete Data

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  • Conference paper
Research in Computational Molecular Biology (RECOMB 2013)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 7821))

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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.

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Author information

Authors and Affiliations

  1. Department of Statistics and Applied Probability, National University of Singapore, Singapore

    Ngoc Hieu Tran & Kwok Pui Choi

  2. Department of Mathematics, National University of Singapore, Singapore

    Kwok Pui Choi & Louxin Zhang

  3. Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore

    Louxin Zhang

Authors
  1. Ngoc Hieu Tran
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  2. Kwok Pui Choi
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  3. Louxin Zhang
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Editor information

Editors and Affiliations

  1. School of Mathematics, Peking University, Beijing, P.R. China

    Minghua Deng

  2. Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, 100084, Beijing, P.R. China

    Rui Jiang

  3. Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA

    Fengzhu Sun

  4. Department of Automation, Tsinghua University, P.O. Box, 100084, Beijing, China

    Xuegong Zhang

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© 2013 Springer-Verlag Berlin Heidelberg

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Tran, N.H., Choi, K.P., Zhang, L. (2013). Counting Motifs in the Entire Biological Network from Noisy and Incomplete Data. In: Deng, M., Jiang, R., Sun, F., Zhang, X. (eds) Research in Computational Molecular Biology. RECOMB 2013. Lecture Notes in Computer Science(), vol 7821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37195-0_24

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  • DOI: https://doi.org/10.1007/978-3-642-37195-0_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37194-3

  • Online ISBN: 978-3-642-37195-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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