  Missing genotypes can affect the efficacy of machine learning approaches to
identify the risk genetic variants of common diseases and traits. The problem
occurs when genotypic data are collected from different experiments with
different DNA microarrays, each being characterised by its pattern of uncalled
(missing) genotypes. This can prevent the machine learning classifier from
assigning the classes correctly. To tackle this issue, we used well-developed
notions of object-attribute biclusters and formal concepts that correspond to
dense subrelations in the binary relation $\textit{patients} \times
\textit{SNPs}$. The paper contains experimental results on applying a
biclustering algorithm to a large real-world dataset collected for studying the
genetic bases of ischemic stroke. The algorithm could identify large dense
biclusters in the genotypic matrix for further processing, which in return
significantly improved the quality of machine learning classifiers. The
proposed algorithm was also able to generate biclusters for the whole dataset
without size constraints in comparison to the In-Close4 algorithm for
generation of formal concepts.
