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A Top-K formal concepts-based algorithm for mining positive and negative correlation biclusters of DNA microarray data

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Abstract

Analyzing and understanding large and complex volumes of biological data is a challenging task as data becomes more widely available. In biomedical research, gene expression data are among the most commonly used biological data. Formal concept analysis frequently identifies deferentially expressed genes in microarray data. Top-K formal concepts are effective at producing effective Formal Concepts. To our knowledge, no existing algorithm can complete the difficult task of identifying only important biclusters. For this purpose, a new Top-K formal concepts-based algorithm for mining biclusters from gene expression data is proposed: Top-BicMiner. It extracts biclusters’ sets with positively and negatively correlated genes according to distinct correlation measures. The proposed method is applied to both synthetic and real-life microarray datasets. The experimental results highlight the Top-BicMiner’s ability to identify statistically and biologically significant biclusters.

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Availability of data and material

links of data used in the experiments are given in the paper.

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Notes

  1. TOPSIS stands for Order of Preference by Similarity to Ideal Solution [36].

  2. We use a separator-free abbreviated form for the sets, e.g., \(\{I_{1}I_{2}I_{3}\}\) stands for the set of items \(\{I_1, I_2, I_3\}\).

  3. The extraction of the formal concepts is carried out through the invocation of the efficient LCM algorithm [72].

  4. nb1 is the number of FCs extracted in Line 9 and nb2 is the number of FCs extracted in Line 10(i.e \(nb=max(nb1,nb2\))).

  5. Available at https://github.com/mehdi-kaytoue/trimax.

  6. Publicly available at http://arep.med.harvard.edu/biclustering/.

  7. Publicly available at http://arep.med.harvard.edu/biclustering/.

  8. Publicly available at http://www.tik.ethz.ch/sop/bimax/.

  9. Available at http://llama.mshri.on.ca/funcassociate/.

  10. The best biclusters have an adjusted p-value less than 0.001%.

  11. Publicly available at https://www.yeastgenome.org/goTermFinder.

  12. http://geneontology.org/.

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Authors and Affiliations

  1. University Mustapha STAMBOULI of Mascara, Mascara, Algeria

    Amina Houari

  2. Department of Software Science, Akadeemia tee 15a, Tallinn University of Technology, Tallinn, Estonia

    Sadok Ben Yahia

  3. Centre for Industrial Software, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Alsion 2, Building A, 1.20, 6400-, Sønderborg, Denmark

    Sadok Ben Yahia

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  1. Amina Houari
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AH contributed to the idea, algorithm, theoretical analysis, writing, and experiments. SB contributed to the theoretical analysis and writing.

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This paper is an extended version of our work proposed in [35].

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Houari, A., Ben Yahia, S. A Top-K formal concepts-based algorithm for mining positive and negative correlation biclusters of DNA microarray data. Int. J. Mach. Learn. & Cyber. 15, 941–962 (2024). https://doi.org/10.1007/s13042-023-01949-9

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