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Developing an effective biclustering technique using an enhanced proximity measure

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Abstract

This paper introduces an enhanced version of Pearson’s correlation coefficient (PCC) to achieve better biclustering-enabled co-expression analysis. The modified measure called local pearson correlation measure (LPCM) helps detect shifting, scaling, and shifting-and-scaling correlation patterns effectively over gene expression data in the presence of outlier. An LPCM-based biclustering technique called local correlation-based biclustering technique (LCBT) has also been proposed to identify biclusters of high biological significance. The biclustering results have been established both statistically and biologically using benchmarked gene expression data.

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Notes

  1. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28.

  2. http://anirbanmukhopadhyay.50webs.com/data.html.

  3. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi.

  4. http://anirbanmukhopadhyay.50webs.com/sporulation.txt.

  5. http://llama.mshri.on.ca/funcassociate/.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Tezpur University, Assam, India

    Pallabi Patowary, Rosy Sarmah & Dhruba K. Bhattacharyya

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  1. Pallabi Patowary
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  2. Rosy Sarmah
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  3. Dhruba K. Bhattacharyya
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Correspondence to Dhruba K. Bhattacharyya.

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Patowary, P., Sarmah, R. & Bhattacharyya, D.K. Developing an effective biclustering technique using an enhanced proximity measure. Netw Model Anal Health Inform Bioinforma 9, 6 (2020). https://doi.org/10.1007/s13721-019-0211-7

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  • DOI: https://doi.org/10.1007/s13721-019-0211-7

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