Year: 2017
Type: article
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.
Author: Khalid Raza
Institution: Jamia Millia Islamia
Language: English
Topic: Rough Sets and Fuzzy Logic
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1726028772