Visualisation and Modelling of High-Dimensional Cancerous Gene Expression Dataset

Abhishek Bhola () and Shailendra Singh ()
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Abhishek Bhola: Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Sector 12, Chandigarh 160012, India
Shailendra Singh: Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Sector 12, Chandigarh 160012, India

Journal of Information & Knowledge Management (JIKM), 2019, vol. 18, issue 01, 1-22

Abstract: The increase in the number of dimensions of cancerous gene expression dataset causes an increase in complexity, misinterpretation and decrease in the visualisation of the particular dataset for further analysis. Therefore, dimensionality reduction, visualisation and modelling tasks of these dataset become challenging. In this paper, a framework is developed which helps to understand, visualise and model high-dimensional cancerous gene expression dataset into lower dimensions which may be helpful in revealing cancer mechanism and diagnosis. Initially, cancerous gene expression datasets are preprocessed to make them complete, precise and efficient; and principal component analysis is applied for dimensionality reduction and visualisation purpose. The regression is used to model the cancerous gene expression dataset so that type of association (linear or nonlinear) and directions between gene profiles may be estimated. To assess the performance of the developed framework, three different types of cancerous gene expression datasets are taken namely: breast (GEO Acc. No. GDS5076), lung (GEO Acc. No. GDS5040) and prostate (GEO Acc. No. GDS5072) which are publicly available. To validate the results of the regression the cross-validation method is used. The results revealed that a linear approach is to be used for prostate cancer dataset and nonlinear approach for breast and lung cancer datasets in finding an association between gene pairs.

Keywords: Gene expression dataset; principal component analysis; singular value decomposition; regression; dimensionality reduction (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219649219500011

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