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

Amina Houari*, Sadok Ben Yahia

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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.

Original languageEnglish
JournalInternational Journal of Machine Learning and Cybernetics
Volume15
Issue number3
Pages (from-to)941-962
ISSN1868-8071
DOIs
Publication statusPublished - Mar 2024

Keywords

  • Biclustering
  • DNA microarray data
  • Formal concept analysis
  • Negative correlation
  • Positive correlation
  • Top-K formal concepts

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