Gene Co-Expression in Mouse Embryo Tissues

Andrews, Simon; McLeod, Kenneth

doi:10.4018/ijiit.2013100104

Cited by 13 publications

(12 citation statements)

References 11 publications

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“…The triples are published online in a resource such as EMAGE 3 . In this paper tissues may be referred to by EMAP IDs rather than their full name.…”

Section: Emage Data Setmentioning

confidence: 99%

“…In [3] authors presented an approach to use FCA to analyse large clusters of gene co-expressions. The approach makes use of the formal context creator FcaBedrock 4 and the formal concept miner In-Close 5 (the aforementioned tools are being redeveloped in CUBIST) to convert and simplify formal contexts.…”

Section: A Conceptual Approach To Gene Expression Analysis Enhanced Bmentioning

confidence: 99%

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A conceptual approach to gene expression analysis enhanced by visual analytics

Melo

Aufaure

Orphanides

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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The analysis of gene expression data is a complex task for biologists wishing to understand the role of genes in the formation of diseases such as cancer. Biologists need greater support when trying to discover, and comprehend, new relationships within their data. In this paper, we describe an approach to the analysis of gene expression data where overlapping groupings are generated by Formal Concept Analysis and interactively analyzed in a tool called CUBIST. The CUBIST workflow involves querying a semantic database and converting the result into a formal context, which can be simplified to make it manageable, before it is visualized as a concept lattice and associated charts.

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“…The triples are published online in a resource such as EMAGE 3 . In this paper tissues may be referred to by EMAP IDs rather than their full name.…”

Section: Emage Data Setmentioning

confidence: 99%

Section: A Conceptual Approach To Gene Expression Analysis Enhanced Bmentioning

confidence: 99%

A conceptual approach to gene expression analysis enhanced by visual analytics

Melo

Aufaure

Orphanides

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…A formal concept is a pair of the form (extent, intent), where extent consists of all objects that share the attributes in intent, and dually the intent consists of all attributes shared by the objects in extent. Formal concepts have a desirable property of being homogeneous and closed in the algebraic sense, which resulted in their extensive use in Gene Expression Analysis (GEA) [ 11 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

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

Ignatov

Khvorykh

Khrunin

et al. 2021

Communications in Computer and Information Science

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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 . 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.

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Section: Introductionmentioning

confidence: 99%

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

Ignatov

Khvorykh

Khrunin

et al. 2020

Preprint

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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 patients x 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.

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Gene Co-Expression in Mouse Embryo Tissues

Cited by 13 publications

References 11 publications

A conceptual approach to gene expression analysis enhanced by visual analytics

A conceptual approach to gene expression analysis enhanced by visual analytics

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

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

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