Object-Attribute Biclustering for Elimination of Missing Genotypes in Ischemic Stroke Genome-Wide Data

TL;DR

This paper presents a biclustering approach to handle missing genotypes in ischemic stroke genome data, improving machine learning classification accuracy by identifying dense subrelations in genotypic matrices.

Contribution

It introduces a biclustering algorithm based on formal concepts to effectively eliminate missing genotypes and enhance classifier performance in large genomic datasets.

Findings

Identified large dense biclusters in genotypic data

Significantly improved machine learning classifier accuracy

Generated biclusters without size constraints

Abstract

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.