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Biclustering models under collinearity in simulated biological experiments

Biclustering models allow simultaneous detection of group observations that are related to variables in a data matrix. Such methods have been applied in biological data for classification. Collinearity is a common feature in biological data as there exist interactions between genes and proteins in t...

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Main Authors: Nnamani, Chibuike, Ahmad, Norhaiza
Format: Article
Language:English
Published: Penerbit UTM Press 2023
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/105445/1/NorhaizaAhmad2023_BiclusteringModelsUnderCollinearityinSimulated.pdf
http://eprints.utm.my/105445/
http://dx.doi.org/10.11113/matematika.v39.n3.1461
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spelling my.utm.1054452024-04-30T07:18:37Z http://eprints.utm.my/105445/ Biclustering models under collinearity in simulated biological experiments Nnamani, Chibuike Ahmad, Norhaiza QA Mathematics Biclustering models allow simultaneous detection of group observations that are related to variables in a data matrix. Such methods have been applied in biological data for classification. Collinearity is a common feature in biological data as there exist interactions between genes and proteins in their respective pathways. These relationships could seriously reduce the efficiency of biclustering models. In this study, synthetic data are generated to investigate the effect of collinearity on the performance of biclustering models. Specifically, the data are generated and induced with varying degrees of collinearity using Cholesky decomposition, and are implanted with biclusters to produce different sets of synthetic data. The effectiveness of three models namely Biclustering by Cheng tecting three types of biclusters in the generated data matrix were compared. The results show that all the models investigated are sensitive to changes in the level of collinearity. At low collinearity, all biclustering models detected the implanted biclusters in the data correctly. However, as the level of collinearity in the data increased, the proportion of detected biclusters captured by the models reduced. In particular at high collinearity, BCCC outperformed the other two models with Jaccard coefficients as high as 0.75 and 0.873 for one and two implanted biclusters respectively. Penerbit UTM Press 2023-12 Article PeerReviewed application/pdf en http://eprints.utm.my/105445/1/NorhaizaAhmad2023_BiclusteringModelsUnderCollinearityinSimulated.pdf Nnamani, Chibuike and Ahmad, Norhaiza (2023) Biclustering models under collinearity in simulated biological experiments. MATEMATIKA, 39 (3). pp. 227-238. ISSN 0127-8274 http://dx.doi.org/10.11113/matematika.v39.n3.1461 DOI:10.11113/matematika.v39.n3.1461
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Nnamani, Chibuike
Ahmad, Norhaiza
Biclustering models under collinearity in simulated biological experiments
description Biclustering models allow simultaneous detection of group observations that are related to variables in a data matrix. Such methods have been applied in biological data for classification. Collinearity is a common feature in biological data as there exist interactions between genes and proteins in their respective pathways. These relationships could seriously reduce the efficiency of biclustering models. In this study, synthetic data are generated to investigate the effect of collinearity on the performance of biclustering models. Specifically, the data are generated and induced with varying degrees of collinearity using Cholesky decomposition, and are implanted with biclusters to produce different sets of synthetic data. The effectiveness of three models namely Biclustering by Cheng tecting three types of biclusters in the generated data matrix were compared. The results show that all the models investigated are sensitive to changes in the level of collinearity. At low collinearity, all biclustering models detected the implanted biclusters in the data correctly. However, as the level of collinearity in the data increased, the proportion of detected biclusters captured by the models reduced. In particular at high collinearity, BCCC outperformed the other two models with Jaccard coefficients as high as 0.75 and 0.873 for one and two implanted biclusters respectively.
format Article
author Nnamani, Chibuike
Ahmad, Norhaiza
author_facet Nnamani, Chibuike
Ahmad, Norhaiza
author_sort Nnamani, Chibuike
title Biclustering models under collinearity in simulated biological experiments
title_short Biclustering models under collinearity in simulated biological experiments
title_full Biclustering models under collinearity in simulated biological experiments
title_fullStr Biclustering models under collinearity in simulated biological experiments
title_full_unstemmed Biclustering models under collinearity in simulated biological experiments
title_sort biclustering models under collinearity in simulated biological experiments
publisher Penerbit UTM Press
publishDate 2023
url http://eprints.utm.my/105445/1/NorhaizaAhmad2023_BiclusteringModelsUnderCollinearityinSimulated.pdf
http://eprints.utm.my/105445/
http://dx.doi.org/10.11113/matematika.v39.n3.1461
_version_ 1797906027399610368
score 13.211869

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