An Efficient Approach for Gene Selection through Parallel Bio-Inspired Algorithms and Shapley Value Analysis
- Title
- An Efficient Approach for Gene Selection through Parallel Bio-Inspired Algorithms and Shapley Value Analysis
- Creator
- Lakshmi Alluri, Vijaya; Pavan Kanadam, Karteeka; Josephine V L, Helen; Rajagopal, Manikandan
- Description
- The fast development of microarray technology has significantly assisted in the use of gene expression analysis to forecast cancer subtypes. Analyzing high-dimensional microarray data is still challenging, as existing hybrid methods cannot find highly discriminative genes. This study aims to use Shapley value analysis and hybrid bio-inspired algorithms to develop a scalable, parallel gene selection technique to increase computing efficiency and classification accuracy in high-dimensional microarray data. This study used hybrid feature selection approaches inspired by bio-organisms to create a scalable parallel gene selection system. The dataset size is initially enlarged by Adaptive Synthetic Sampling (ADASYN). They use the Recursive Feature Elimination (RFE) approach to extract features and determine their Shapley values. In addition, the Whale Optimization Algorithm (WOA) works to determine which genes are most important. After that, Machine Learning (ML) techniques assist in classifying the chosen characteristics. According to the experiment results, the suggested strategy surpasses standard gene selection techniques with the same datasets, employing improved classification accuracy and reducing computing time. K-NN achieved an accuracy of 85.44%, while LR showed improved results with an accuracy of 91.72%. RF further increased accuracy to 94.69%. SVM demonstrated exceptional performance, reaching an accuracy of 97.63%. Ultimately, XGBoost excelled among all models with the highest accuracy of 98.49%, highlighting its robust ability to classify SRBCT samples effectively based on gene expression data. 2025, Ayandegan Institute of Higher Education. All rights reserved.
- Source
- International Journal of Research in Industrial Engineering;Volume;14;Issue;3;pp.508-527
- Date
- 01-01-2025
- Publisher
- Ayandegan Institute of Higher Education
- Subject
- Bio-inspired algorithms; High-dimensional data; Machine learning; Microarray technology; Shapley value analysis
- Coverage
- Lakshmi Alluri V., Department of Computer Science and Engineering, Acharya Nagarjuna University, Andhra Pradesh, Guntur, India; Pavan Kanadam K., Department of Computer Applications, R.V.R & J.C College of Engineering, Andhra Pradesh, Guntur, India; Josephine V L H., Department of Business Analytics, School of Business and Management, Christ University, Karnataka, Bangalore, India; Rajagopal M., Department of Lean Operations and Systems, School of Business and Management, Christ University, Karnataka, Bengaluru, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 27831337;
- Format
- online
- Language
- English
- Type
- Article
Collection
Citation
Lakshmi Alluri, Vijaya; Pavan Kanadam, Karteeka; Josephine V L, Helen; Rajagopal, Manikandan, “An Efficient Approach for Gene Selection through Parallel Bio-Inspired Algorithms and Shapley Value Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/23395.