Research on optimization of two-way clustering algorithm for gene expression data analysis

Weiheng Nie

doi:10.1117/12.2692181

7 September 2023 Research on optimization of two-way clustering algorithm for gene expression data analysis

Weiheng Nie

Proceedings Volume 12789, International Conference on Modern Medicine and Global Health (ICMMGH 2023); 127890G (2023) https://doi.org/10.1117/12.2692181
Event: International Conference on Modern Medicine and Global Health (ICMMGH 2023), 2023, Oxford, United Kingdom

Abstract

Nowadays, as a high-throughput experimental method, gene chips can obtain tens of thousands of genetic data in one experiment, which can be used to analyze the regulation of gene expression profiles, diagnosis and treatment of diseases, and the development of new drugs. However, gene chip technology also produces a large amount of complex data, and the main problem we are currently facing is how to manage and analyze these data and mine meaningful biological information from them. In this paper, based on the comparative analysis of unidirectional and bidirectional clustering algorithms, a bidirectional clustering method combining unidirectional clustering and sparse singular value decomposition (SSVD) is proposed, and its effectiveness is analyzed. The improvement of the SSVD method is to use the single-way clustering method instead of manually setting parameters, and use the results obtained by the single-way clustering to ensure that the clustering of the genes is performed while the sample clustering has certain correctness. Experiments show that compared with the traditional SSVD algorithm, it has a better execution effect.

Citation Download Citation

Weiheng Nie "Research on optimization of two-way clustering algorithm for gene expression data analysis", Proc. SPIE 12789, International Conference on Modern Medicine and Global Health (ICMMGH 2023), 127890G (7 September 2023); https://doi.org/10.1117/12.2692181

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