  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.
