Pathway-Based Analysis Using SVM-RFE for Gene Selection and Classification

Nurazreen Afiqah A. Rahman, Nurul Athirah Nasarudin, Mohd Saberi Mohamad

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The pathway-based analysis is one method for selecting and classifying genes by incorporating pathway information. Integration of pathway knowledge into microarray data significantly advances researchers in the analysis of complex diseases. Microarray data involves thousands of genes to be selected, and therefore, a suitable method to eliminate noisy and uninformative genes is needed. Selecting significant genes for a specific disease is crucial to identifying genes highly related to disease production. Previous research shows that using both pathway information and gene expression data is more significant in disease identification. Therefore, pathway-based analysis using Support Vector Machine Recursive Feature Elimination (SVM-RFE) is introduced in this research to identify significant genes associated with analyzing the targeted phenotype. The datasets involved in this research are lung cancer and gender dataset. The results from the proposed method performed better than previous work as the significant genes are selected from the highest rank of genes in the highest rank of the pathway. The performance of the proposed method was evaluated using 10-fold cross-validation in terms of accuracy. Finally, a biological validation was conducted on selected genes in the top 5 pathways based on biological literature.

Original languageEnglish
Title of host publicationAI Technologies and Virtual Reality - Proceedings of 7th International Conference on Artificial Intelligence and Virtual Reality AIVR 2023
EditorsKazumi Nakamatsu, Srikanta Patnaik, Roumen Kountchev
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9789819990177
Publication statusPublished - 2024
Event7th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2023 - Kumamoto, Japan
Duration: Jul 21 2023Jul 23 2023

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


Conference7th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2023


  • Artificial intelligence
  • Bioinformatics
  • Data science
  • Gene selection
  • Pathway-based analysis
  • Support vector machine recursive feature elimination (SVM-RFE)

ASJC Scopus subject areas

  • General Decision Sciences
  • General Computer Science


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