Formal Concept Analysis for Knowledge Discovery from Biological Data

TL;DR

This paper reviews how Formal Concept Analysis (FCA) is applied to analyze large biological datasets, aiding in gene expression analysis, network discovery, and classification tasks, with a discussion of existing tools and challenges.

Contribution

It provides a comprehensive overview of FCA applications in biological data analysis and discusses current challenges and software tools in the field.

Findings

FCA effectively aids gene expression discretization and clustering.

FCA-based tools are actively used in biological data analysis.

Challenges include scalability and data complexity.

Abstract

Due to rapid advancement in high-throughput techniques, such as microarrays and next generation sequencing technologies, biological data are increasing exponentially. The current challenge in computational biology and bioinformatics research is how to analyze these huge raw biological data to extract biologically meaningful knowledge. This review paper presents the applications of formal concept analysis for the analysis and knowledge discovery from biological data, including gene expression discretization, gene co-expression mining, gene expression clustering, finding genes in gene regulatory networks, enzyme/protein classifications, binding site classifications, and so on. It also presents a list of FCA-based software tools applied in biological domain and covers the challenges faced so far.