Title: Formal concept analysis for knowledge discovery from biological data

Authors: Khalid Raza

Addresses: Department of Computer Science, Jamia Millia Islamia, New Delhi, India

Abstract: Owing to the rapid advancement in high-throughput technologies, such as microarrays and next generation sequencing, the volume of biological data is increasing exponentially. The current challenge in computational biology and bioinformatics research is how to analyse these huge raw biological datasets to extract meaningful biological knowledge. Formal concept analysis is a method based on lattice theory and widely used for data analysis, knowledge representation, knowledge discovery and knowledge management across several domains. This paper reviews the applications of formal concept analysis for knowledge discovery from biological data, including gene expression discretisation, gene co-expression mining, gene expression clustering, finding genes in gene regulatory networks, enzyme/protein classifications, binding site classifications, and domain-domain interaction. It also presents a list of FCA-based software tools applied to the biological domain, and covers the challenges and future directions in this field.

Keywords: formal concept analysis; microarray analysis; gene expression mining; concept lattice; biological databases.

DOI: 10.1504/IJDMB.2017.088138

International Journal of Data Mining and Bioinformatics, 2017 Vol.18 No.4, pp.281 - 300

Accepted: 26 Jul 2017
Published online: 21 Nov 2017 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article