Object-Attribute Biclustering for Elimination of Missing Genotypes in Ischemic Stroke Genome-Wide Data
| Autor: | Gennady V. Khvorykh, Andrey Khrunin, E. A. Koltsova, Dmitry I. Ignatov, Fouzi Takelait, Elizaveta A. Petrova, Stefan Nikolić, Dmitrii Egurnov, Makhmud Shaban |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning J.3 Computer science Object Attribute Single-nucleotide polymorphism Genome Statistics - Applications Article Machine Learning (cs.LG) Biclustering I.2.6 I.5.3 I.2.1 Missing genotypes Genotype Quantitative Biology - Genomics Applications (stat.AP) Data mining Genomics (q-bio.GN) Learning classifier system Ischemic stroke business.industry Binary relation Formal concept analysis Pattern recognition Single nucleotide polymorphism ComputingMethodologies_PATTERNRECOGNITION FOS: Biological sciences Artificial intelligence DNA microarray business 92D20 62H30 68T10 |
| Zdroj: | Recent Trends in Analysis of Images, Social Networks and Texts |
| Popis: | 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. Comment: Accepted to AIST 2020 |
| Databáze: | OpenAIRE |
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