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Rank-preserving biclustering algorithm: a case study on miRNA breast cancer.

Effective biomarkers aid in the early diagnosis and monitoring of breast cancer and thus play an important role in the treatment of patients suffering from the disease. Growing evidence indicates that alteration of expression levels of miRNA is one of the principal causes of cancer. We analyze breas...

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Published in:Medical & Biological Engineering & Computing Vol. 59; no. 4; pp. 989 - 1005
Main Authors: Mandal, Koyel, Sarmah, Rosy, Bhattacharyya, Dhruba Kumar, Kalita, Jugal Kumar, Borah, Bhogeswar
Format: Journal Article
Published: Springer Nature Apr2021
Online Access:View this record in EBSCOhost
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      dt: Apr2021
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      pub: Springer Nature
      place: New York, New York
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        NLM33840048
        10.1007/s11517-020-02271-0
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        atl: Rank-preserving biclustering algorithm: a case study on miRNA breast cancer.
      aug:
        au:
          Mandal, Koyel
          Sarmah, Rosy
          Bhattacharyya, Dhruba Kumar
          Kalita, Jugal Kumar
          Borah, Bhogeswar
        affil: Department of Computer Science and Engineering, Tezpur University, Assam, India
      sug:
        subj:
          Breast Neoplasms
          RNA
          Algorithms
          Female
          Gene Expression Profiling
          Scales
          Female
      ab: Effective biomarkers aid in the early diagnosis and monitoring of breast cancer and thus play an important role in the treatment of patients suffering from the disease. Growing evidence indicates that alteration of expression levels of miRNA is one of the principal causes of cancer. We analyze breast cancer miRNA data to discover a list of biclusters as well as breast cancer miRNA biomarkers which can help to understand better this critical disease and take important clinical decisions for treatment and diagnosis. In this paper, we propose a pattern-based parallel biclustering algorithm termed Rank-Preserving Biclustering (RPBic). The key strategy is to identify rank-preserved rows under a subset of columns based on a modified version of all substrings common subsequence (ALCS) framework. To illustrate the effectiveness of the RPBic algorithm, we consider synthetic datasets and show that RPBic outperforms relevant biclustering algorithms in terms of relevance and recovery. For breast cancer data, we identify 68 biclusters and establish that they have strong clinical characteristics among the samples. The differentially co-expressed miRNAs are found to be involved in KEGG cancer related pathways. Moreover, we identify frequency-based biomarkers (hsa-miR-410, hsa-miR-483-5p) and network-based biomarkers (hsa-miR-454, hsa-miR-137) which we validate to have strong connectivity with breast cancer. The source code and the datasets used can be found at http://agnigarh.tezu.ernet.in/~rosy8/Bioinformatics_RPBic_Data.rar . Graphical Abstract.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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