An improved MOPSO approach with adaptive strategy for identifying biomarkers from gene expression dataset

Shuaiqun Wang; Tianshun Zhang; Wei Kong; Gen Wen; Yaling Yu; Shuaiqun Wang; Tianshun Zhang; Wei Kong; Gen Wen; Yaling Yu

doi:10.3934/mbe.2023072

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 1580-1598. doi: 10.3934/mbe.2023072

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An improved MOPSO approach with adaptive strategy for identifying biomarkers from gene expression dataset

1.
College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, China
2.
Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
3.
Institute of Microsurgery on Extremities, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China

Academic Editor: Shangce Gao
† Shuaiqun Wang and Tianshun Zhang should be regarded as joint first authors.

Received: 03 September 2022 Revised: 16 October 2022 Accepted: 20 October 2022 Published: 03 November 2022

Biomarkers plays an important role in the prediction and diagnosis of cancers. Therefore, it is urgent to design effective methods to extract biomarkers. The corresponding pathway information of the microarray gene expression data can be obtained from public database, which makes possible to identify biomarkers based on pathway information and has been attracted extensive attention. In the most existing methods, all the member genes in the same pathway are regarded as equally important for inferring pathway activity. However, the contribution of each gene should be different in the process of inferring pathway activity. In this research, an improved multi-objective particle swarm optimization algorithm with penalty boundary intersection decomposition mechanism (IMOPSO-PBI) has been proposed to quantify the relevance of each gene in pathway activity inference. In the proposed algorithm, two optimization objectives namely t-score and z-score respectively has been introduced. In addition, in order to solve the problem that optimal set with poor diversity in the most multi-objective optimization algorithms, an adaptive mechanism for adjusting penalty parameters based on PBI decomposition has been introduced. The performance of the proposed IMOPSO-PBI approach compared with some existing methods on six gene expression datasets has been given. To verify the effectiveness of the proposed IMOPSO-PBI algorithm, experiments were carried out on six gene datasets and the results has been compared with the existing methods. The comparative experiment results show that the proposed IMOPSO-PBI method has a higher classification accuracy and the extracted feature genes are verified possess biological significance.
- pathway activity,
- multi-objective optimization,
- particle swarm optimization,
- adaptive strategy,
- PBI decomposition mechanism
Citation: Shuaiqun Wang, Tianshun Zhang, Wei Kong, Gen Wen, Yaling Yu. An improved MOPSO approach with adaptive strategy for identifying biomarkers from gene expression dataset[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 1580-1598. doi: 10.3934/mbe.2023072

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Abstract

Biomarkers plays an important role in the prediction and diagnosis of cancers. Therefore, it is urgent to design effective methods to extract biomarkers. The corresponding pathway information of the microarray gene expression data can be obtained from public database, which makes possible to identify biomarkers based on pathway information and has been attracted extensive attention. In the most existing methods, all the member genes in the same pathway are regarded as equally important for inferring pathway activity. However, the contribution of each gene should be different in the process of inferring pathway activity. In this research, an improved multi-objective particle swarm optimization algorithm with penalty boundary intersection decomposition mechanism (IMOPSO-PBI) has been proposed to quantify the relevance of each gene in pathway activity inference. In the proposed algorithm, two optimization objectives namely t-score and z-score respectively has been introduced. In addition, in order to solve the problem that optimal set with poor diversity in the most multi-objective optimization algorithms, an adaptive mechanism for adjusting penalty parameters based on PBI decomposition has been introduced. The performance of the proposed IMOPSO-PBI approach compared with some existing methods on six gene expression datasets has been given. To verify the effectiveness of the proposed IMOPSO-PBI algorithm, experiments were carried out on six gene datasets and the results has been compared with the existing methods. The comparative experiment results show that the proposed IMOPSO-PBI method has a higher classification accuracy and the extracted feature genes are verified possess biological significance.

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