SavedAdd your journal

Journals / Gazi University Journal of Science / 2026 / Volume: 39 Issue: 2

Target Gene Prediction From Microarray Data Using Data Mining Methods

Zerrin Yıldız Çavdar(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, MÜHENDİSLİK FAKÜLTESİ)0000-0003-4299-5344
Ahmet Sertbaş(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, MÜHENDİSLİK FAKÜLTESİ)0000-0001-8166-1211
Tolga Ensari(Arkansas Tech University, Engineering and Computing Sciences)0000-0003-0896-3058
Müberra Nur Akçaman(ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-6428-3826
Pages
—
Publication Date
2026-06-01
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The study is concluded with the listing of the most affected genes among the result genes.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Many different techniques such as microarray, microrna, RNA sequencing and parallel sequencing are used in biomedical research.
📊
Key Findings & Data:The method we developed for analysing microarray data is applied separately to both data sets and led to successful results.
💡
Conclusion & Impact:The study is concluded with the listing of the most affected genes among the result genes.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Microarray analyzing · Gene expression analyzing · Target gene prediction

Abstract

Many different techniques such as microarray, microrna, RNA sequencing and parallel sequencing are used in biomedical research. Among these biotechnological approaches, microarray is widely used for analysing data such as DNA, RNA or proteins. Microarray technology offers advantages in many areas such as analysing gene expression, mutation analysis, epigenetic studies or biomarker discovery. The use of artificial intelligence methods in the analysis of large amounts of data, such as microarray data, offers a gain in accuracy and speed. In this study, gene expression analysis of microarray data is performed using data mining methods. Freely available datasets are used for the study. The first one is the microarray dataset investigating the effects of chronic hypoxia treatment on the brain of mice. The second is a microarray dataset that examines the changes in mouse neurons exposed to oxidative stress. The method we developed for analysing microarray data is applied separately to both data sets and led to successful results. In this work, after the datasets are made suitable for processing in a computer environment, the prediction process is developed using data mining methods. The study is concluded with the listing of the most affected genes among the result genes.

🔖

Cite This Article

Çavdar, Z. Y., Sertbaş, A., Ensari, T., Akçaman, M. N. (2026). Target Gene Prediction From Microarray Data Using Data Mining Methods. Gazi University Journal of Science, Volume: 39 Issue: 2. https://doi.org/10.35378/gujs.1710419

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (27)

  1. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., and Lander, E.S., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring”, Science, 531-537, (1999). DOI: https://doi.org/10.1126/science.286.5439.531
  2. Hanahan, D., and Weinberg, R.A., “Hallmarks of cancer: the next generation”, Cell, 646-674, (2011). DOI: https://doi.org/10.1016/j.cell.2011.02.013
  3. Collins, F.S., and Varmus, H., “A new initiative on precison medicine”, The New England Journal of Medicine, 793-795, (2015). DOI: https://doi.org/10.1056/NEJMp1500523
  4. Çavdar, Z.Y., Ensari, T., Sener, L.T., and Sertbaş, A., “Makine öğrenmesi tabanlı mikrodizi tekniği ile mikrorna hedef tahmini: araştırma çalışması”, Avrupa Bilim ve Teknoloji Dergisi, 39-45, (2022). DOI: https://doi.org/10.31590/ejosat.1220962
  5. Wang, Z., Gerstein, M., and Snyder, M., “RNA-Seq: a revolutionary tool for transcriptomics”, Nature reviews genetics, 57-63, (2009). DOI: https://doi.org/10.1038/nrg2484
  6. Bustin, S.A., “Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays”, Journal of molecular endocrinology, 169-193, (2000). DOI: https://doi.org/10.1677/jme.0.0250169
  7. Schena, M., Shalon, D., Davis, R.W., and Brown, P.O., “Quantitative monitoring of gene expression patterns with a complementary DNA microarray”, Science, 467-470, (1995). DOI: https://doi.org/10.1126/science.270.5235.467
  8. Illumina BeadStudio, https://www.illumina.com (Access Date: 23.09.2024).
  9. Affymetrix, https://www.thermofisher.com/tr/en/home/life-science/microarray-analysis.html (Access Date: 16.09.2024).
  10. Partex, https://www.partek.com/ (Access Date: 16.09.2024).
  11. GeneSpring, https://www.agilent.com/en/product/software-informatics/genomics-software-informatics/gene-expression/genespring-gx (Access Date: 16.09.2024).
  12. National Center of Biotechnology Information, https://ncb.nlm.nih.gov (Access Date: 22.05.2024).
  13. Bioconductor, https://www.bioconductor.org/ (Access Date: 22.05.2024).
  14. R Programming Language, https://www.r-project.org/ (Access Date: 22.05.2024).
  15. GSE8262, ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8262 (Access Date: 05.06.2024).
  16. Zhou, D., Wang, J., Zapala, M.A., Xue, J., Schork, N.J., and Haddad, G.G., “Gene expression in mouse brain following chronic hypoxia: role of sarcospan in glial cell death”, Physiol Genomics, 370-379, (2008). DOI: https://doi.org/10.1152/physiolgenomics.00147.2007
  17. Wang, H., Yang, T., Sun, J., Zhang, S., and Liu, S., “SENP1 modulates microglia-mediated neuroinflammation toward intermittent hypoxia-induced cognitive decline through the de-SUMOylation of NEMO”, Journal of Cellular and Molecular Medicine, 6463-7099, (2021). DOI: https://doi.org/10.1111/jcmm.16844
  18. Wang, H., Wang, X., Shen, Y., Wang, Y., Yang, T., Sun, J., and Liu, S., “SENP1 modulates chronic intermittent hypoxia-induced inflammation of microglia and neuronal injury by inhibiting TOM1 pathway”, International Immunopharmacology, 1-13, (2023). DOI: https://doi.org/10.1016/j.intimp.2023.110230
  19. Çavdar, Z.Y., Sertbaş, A., and Ensari, T., “Hedef gen çıkarımı için makine öğrenmesi tabanlı mikrodizi verilerinin analizi”, Bilgisayar Bilimleri ve Mühendisliği Dergisi, 174-183, (2024). DOI: https://doi.org/10.54525/bbmd.1510383
  20. GSE10470, ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10470 (Access Date: 05.06.2024).
  21. Lange, P.S., Chavez, J.C., Pinto, J.T., Coppola, G., Sun. C.W., Townes, T.M., Geschwind, D.H., and Ratan, R.R., “ATF4 is an oxidative stress-inducible, prodeath transcription factor in neurons in vitro and in vivo”, The Journal of Exprerimental Medicine, 1227-1242, (2008). DOI: https://doi.org/10.1084/jem.20071460
  22. Woo, M.S., Ufer, F., Rothammer, N., Liberto, G.D., Binkle, L., Haferkamp, U., Sonner, J.K., Engler, J.B., Hornig, S., Bauer, S., Wagner, I., Egervari, K., Raber, J., Duvoisin, R.M., Pless, O., Merkler, D., and Friese, M.A., “Neuronal metabotropic glutamate receptor 8 protects against neurodegeneration in CNS inflammation”, The Journal of Exprerimental Medicine, 1-30, (2021). DOI: https://doi.org/10.1084/jem.20201290
  23. Bloomingdale, P., Meregalli, C., Pollard, K., Canta, A., Chiorazzi, A., Fumagalli, G., Monza, L., Pozzi, E., Alberti, P., Ballarini, E., Oggioni, N., Carlson, L., Liu, W., Ghandili, M., Ignatowski, T.A., Lee, K.P., Moore, M.J., Cavaletti, G., and Mager, D.E., “Systems pharmacology modeling identifies a novel treatment strategy for bortezomib-induced neuropathic pain”, Frontiers in Pharmacology, 1-16, (2022). DOI: https://doi.org/10.3389/fphar.2021.817236
  24. Bishara, A.J., and Hittner, J.B., “Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches”, Psychological Methods, 399, (2012). DOI: https://doi.org/10.1037/a0028087
  25. Bolstad, B.M., Irizarry, R.A., Åstrand, M., and Speed, T.P., “A comparison of normalization methods for high density oligonucleotide array data based on variance and bias”, Bioinformatics, 185-193, (2003). DOI: https://doi.org/10.1093/bioinformatics/19.2.185
  26. Smyth, G.K., “Linear models and empirical bayes methods for assessing differential expression in microarray experiments”, Statistical Applications in Genetics and Molecular Biology, (2004). DOI: https://doi.org/10.2202/1544-6115.1027
  27. Pawitan, Y., Michiels, S., Koscielny, S., Gusnanto, A., and Ploner, A., “False discovery rate, sensitivity and sample size for microarray studies”, Bioinformatics, 3017-3024, (2005). DOI: https://doi.org/10.1093/bioinformatics/bti448