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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Enthusiastic : International Journal of Applied Statistics and Data Science Automotive Experiences Bulletin of Applied Mathematics and Mathematics Education STATISTIKA Emerging Statistics and Data Science Journal
Yotenka, Rahmadi
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Classification of Tumor and Normal Tissue Gene Expression in Lung Adenocarcinoma Using Support Vector Machine and Gaussian Process Classification Yotenka, Rahmadi; Effendie, Adhitya Ronnie; Fajriyah, Rohmatul
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11763

Abstract

Lung adenocarcinoma (LUAD) is a major cause of cancer-related mortality worldwide. This study aims to identify potential LUAD biomarkers and develop robust classification models using the GSE151101 microarray dataset. Preprocessing included RMA normalization, ComBat batch-effect correction, and feature filtering based on annotation completeness, variability, and statistical significance. Support Vector Machine (SVM) and Gaussian Process Classification (GPC) models were constructed, with the polynomial GPC model achieving the best performance (accuracy 97.92%; F1-score 97.96%). Repeated 10-fold cross-validation confirmed its stability (mean accuracy 96.88%, SD ±1.97%, CV 2.03%), outperforming linear SVM, GPC-RBF, and Multiple Kernel Learning (MKL). Functional enrichment analysis showed that key discriminative genes; CDH13, CDKN2A, BCL2L11, MYL9, COL1A1, and AKT3; were enriched in pathways related to epithelial–mesenchymal transition, extracellular matrix remodelling, focal adhesion, PI3K/AKT signalling, and cell-cycle regulation, all of which are central to LUAD progression. In general, polynomial-kernel GPC is a stable and useful way to classify transcriptomes and rank biomarkers. Nevertheless, the translational potential of these signatures requires further validation in independent and clinically controlled cohorts.
Co-Authors Adhitya Ronnie Effendie, Adhitya Ronnie Agustinus Purna Irawan Akbar, Wahyu Sa'dun Anindita, Sukma Ayudhaya, Wanvisa Saisanan Na Cahyaningrum, Nafisah Hanan Cionita, Tezara Danarwindu, Ghiffari Ahnaf Fitriyana, Deni Fajar Ismail, Nur Hidayah Kireyna Cindy Pradhisa, Kireyna Kongchouy, Noodchanath Maharani, Shafira Mas'udi, Nafisah Mas'ud Muhammad Khafidh Putera, Finny Pratama Rifky Ismail Rohmatul Fajriyah Siregar, Januar Parlaungan Suparna Yovita, Zulma Zulkarnaen Anas, Irfan