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Bladder Cancer Microarray Analysis and Biomarker Discovery Using Machine Learning

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Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023 (AISI 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 184))

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Abstract

One of the most prevalent types of malignant cancers is bladder cancer (BC), which also happens to be the most widespread genitourinary cancer globally. The development and progression of BC are influenced significantly by both genetic and environmental factors. The objective of this study was to identify biomarker genes using three approaches: statistical analysis, biological packages, and machine learning and ensure that the machine learning is a robust way to identify the differential Gene expression. The study utilized GSE7476 expression profiles, which were obtained by downloading data from the Gene Expression Omnibus (GEO) database. ElasticNet was used as one of the methods to identify biomarker genes. The accuracy of the results was assessed by testing on unseen samples, and a perfect accuracy of 100% was achieved. Additionally, the identified biomarker genes were further analyzed using the Go ontology pathway to understand their functional significance and potential involvement in biological processes. Lastly, we compared the pathways associated with the ElasticNet method to those of state-of-the-art methods. This comprehensive approach provides a robust and reliable method for biomarker identification in the context of bladder cancer research using machine learning.

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Authors and Affiliations

  1. Scientific Computing Department Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Moshira S. Ghaleb, Hala M. Ebied & Mohamed F. Tolba

Authors
  1. Moshira S. Ghaleb
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  2. Hala M. Ebied
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  3. Mohamed F. Tolba
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Corresponding author

Correspondence to Moshira S. Ghaleb .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    AboulElla Hassanien

  2. Port Said University, Port Fouad City, Egypt

    Rawya Y. Rizk

  3. Department of Operations Research and Statistics, The University of Belgrade, Faculty of Organizational Sciences, Belgrade, Serbia

    Dragan Pamucar

  4. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  5. Shulin District, Fujian University of Technology, New Taipei, Taiwan

    Kuo-Chi Chang

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Ghaleb, M.S., Ebied, H.M., Tolba, M.F. (2023). Bladder Cancer Microarray Analysis and Biomarker Discovery Using Machine Learning. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_25

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