Formal Concept Analysis for Knowledge Discovery from Biological Data
It targets researchers in computational biology and bioinformatics dealing with exponential data growth, but it is incremental as it reviews existing methods rather than introducing new ones.
This review paper addresses the challenge of analyzing large biological datasets by presenting applications of formal concept analysis (FCA) for knowledge discovery, including tasks like gene expression discretization and clustering, without reporting specific numerical results.
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.