A Review of Artificial Intelligence Techniques for Biomarker Discovery
DOI:
https://doi.org/10.68903/vjta.2025.v06i02.05Keywords:
Biomarker Discovery, Artificial Intelligence, Machine Learning, Deep Learning, Explainable AIAbstract
Biomarkers play a crucial role in medical research, helping guide diagnosis, prognosis, and treatment decisions. Traditionally, discovering these biomarkers has been a labor-intensive process, relying on experiments and statistical analysis that often capture only a small part of biological complexity. The introduction of Artificial Intelligence has brought a significant shift in this field. Machine learning approaches first allowed researchers to uncover patterns in genomic and clinical data that were previously difficult to recognize, and later, deep learning expanded these possibilities to include multi-omics data and medical imaging. Recently, explainable AI techniques have addressed the challenge of trust and interpretability, enabling clinicians to understand and validate AI-derived biomarkers. This review presents the journey of biomarker discovery with AI, highlighting key developments, current trends, and challenges, while discussing the potential for AI to enhance the clinical impact of biomarker research.
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References
Al Abir Fuad, Shovan S. M., Hasan Md. Al Mehedi, Sayeed Abu, & Shin Jungpil. (2022). Biomarker identification by reversing the learning mechanism of an autoencoder and recursive feature elimination - Molecular Omics (RSC Publishing). Molecular Omics, 18, 652–661. https://doi.org/DOI:10.1039/D1MO00467K DOI: https://doi.org/10.1039/D1MO00467K
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015). Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnology, 33(8), 831–838. https://doi.org/10.1038/nbt.3300 DOI: https://doi.org/10.1038/nbt.3300
Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. (2017). DeepCpG: Accurate prediction of single-cell DNA methylation states using deep learning. Genome Biology, 18(1), 1–13. https://doi.org/10.1186/S13059-017-1189-Z/FIGURES/5 DOI: https://doi.org/10.1186/s13059-017-1189-z
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular Systems Biology, 12(7). https://doi.org/10.15252/MSB.20156651 DOI: https://doi.org/10.15252/msb.20156651
Bedi, P., Rani, S., Gupta, B., Bhasin, V., & Gole, P. (2025). EpiBrCan-Lite: A lightweight deep learning model for breast cancer subtype classification using epigenomic data. Computer Methods and Programs in Biomedicine, 260, 108553. https://doi.org/10.1016/J.CMPB.2024.108553 DOI: https://doi.org/10.1016/j.cmpb.2024.108553
Bote-Curiel, L., García, M., & Pérez, J. (2021). Ovarian cancer data analysis using deep learning. ScienceDirect.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Classification and Regression Trees, 1–358. https://doi.org/10.1201/9781315139470/CLASSIFICATION-REGRESSION-TREES-LEO-BREIMAN-JEROME-FRIEDMAN-OLSHEN-CHARLES-STONE/RIGHTS-AND-PERMISSIONS DOI: https://doi.org/10.1201/9781315139470-1
Chaudhary, K., Poirion, O. B., Lu, L., & Garmire, L. X. (2018). Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clinical Cancer Research : An Official Journal of the American Association for Cancer Research, 24(6), 1248–1259. https://doi.org/10.1158/1078-0432.CCR-17-0853 DOI: https://doi.org/10.1158/1078-0432.CCR-17-0853
Chen, X., Zhang, Y., & Li, J. (2022). Transformer models for biomarker discovery in gastric cancer. PMC Central.
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018). Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nature Medicine, 24(10), 1559–1567. https://doi.org/10.1038/S41591-018-0177-5 DOI: https://doi.org/10.1038/s41591-018-0177-5
Cover, T. M., & Hart, P. E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964 DOI: https://doi.org/10.1109/TIT.1967.1053964
Cox, D. R. (1972). Regression Models and Life-Tables in the Chair]. Research Section, on Wednesday. https://academic.oup.com/jrsssb/article/34/2/187/7027194 DOI: https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., van den Driessche, G., Lakshminarayanan, B., Meyer, C., Mackinder, F., Bouton, S., Ayoub, K., Chopra, R., King, D., Karthikesalingam, A., … Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342–1350. https://doi.org/10.1038/S41591-018-0107-6 DOI: https://doi.org/10.1038/s41591-018-0107-6
Díaz-Uriarte, R., & Alvarez de Andrés, S. (2006). Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7(1), 1–13. https://doi.org/10.1186/1471-2105-7-3/FIGURES/1 DOI: https://doi.org/10.1186/1471-2105-7-3
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/NATURE21056;SUBJMETA DOI: https://doi.org/10.1038/nature21056
Freund, Y., & Schapire, R. E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139. https://doi.org/10.1006/JCSS.1997.1504 DOI: https://doi.org/10.1006/jcss.1997.1504
Friedman, N., Linial, M., Nachman, I., & Pe’er, D. (2000). Using Bayesian networks to analyze expression data. Journal of Computational Biology : A Journal of Computational Molecular Cell Biology, 7(3–4), 601–620. https://doi.org/10.1089/106652700750050961 DOI: https://doi.org/10.1089/106652700750050961
Guo, Y., Wang, L., & Zhang, H. (2020). Biomarker identification in lung cancer using autoencoders. BMC Bioinformatics, 21, 1–9.
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1–3), 389–422. https://doi.org/10.1023/A:1012487302797/METRICS DOI: https://doi.org/10.1023/A:1012487302797
Hao, J., Masum, M., Oh, J. H., & Kang, M. (2019). Gene- and Pathway-Based Deep Neural Network for Multi-omics Data Integration to Predict Cancer Survival Outcomes. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11490 LNBI, 113–124. https://doi.org/10.1007/978-3-030-20242-2_10/TABLES/4 DOI: https://doi.org/10.1007/978-3-030-20242-2_10
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1312. https://doi.org/10.1002/WIDM.1312 DOI: https://doi.org/10.1002/widm.1312
Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. Applied Logistic Regression. https://doi.org/10.1002/0471722146 DOI: https://doi.org/10.1002/0471722146
Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1), 1–12. https://doi.org/10.1186/S12874-018-0482-1/FIGURES/6 DOI: https://doi.org/10.1186/s12874-018-0482-1
Lee, S., Kim, H., & Lee, J. (2020). Multi-omics deep learning for colorectal cancer biomarkers. PMC Central.
Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews. Genetics, 16(6), 321–332. https://doi.org/10.1038/NRG3920 DOI: https://doi.org/10.1038/nrg3920
Lin, Y., & Wang, J. (2020). Classifying breast cancer subtypes using deep neural networks. PMC Central. DOI: https://doi.org/10.3390/genes11080888
Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in Bioinformatics, 18(5), 851–869. https://doi.org/10.1093/BIB/BBW068 DOI: https://doi.org/10.1093/bib/bbw068
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Scientific Reports, 6(1), 1–10. https://doi.org/10.1038/SREP26094;TECHMETA DOI: https://doi.org/10.1038/srep26094
Qiu, W. R., Qi, B. B., Lin, W. Z., Zhang, S. H., Yu, W. K., & Huang, S. F. (2022). Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data. Frontiers in Genetics, 13, 926927. https://doi.org/10.3389/FGENE.2022.926927/BIBTEX DOI: https://doi.org/10.3389/fgene.2022.926927
Rifai, N., Gillette, M. A., & Carr, S. A. (2006). Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nature Biotechnology, 24(8), 971–983. https://doi.org/10.1038/NBT1235 DOI: https://doi.org/10.1038/nbt1235
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision, 2017-October, 618–626. https://doi.org/10.1109/ICCV.2017.74 DOI: https://doi.org/10.1109/ICCV.2017.74
Strimbu, K., & Tavel, J. A. (2010). What are biomarkers? Current Opinion in HIV and AIDS, 5(6), 463–466. https://doi.org/10.1097/COH.0B013E32833ED177 DOI: https://doi.org/10.1097/COH.0b013e32833ed177
Tan, J., Ung, M., Cheng, C., & Greene, C. S. (2015). Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. Pacific Symposium on Biocomputing, 132–143. https://doi.org/10.1142/9789814644730_0014 DOI: https://doi.org/10.1142/9789814644730_0014
Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267–288. https://doi.org/10.1111/J.2517-6161.1996.TB02080.X DOI: https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Verma, R., Cohen, M., Toro, P., Mokhtari, M., & Tiwari, P. (2021). NIMG-60. PREDICTING OVERALL SURVIVAL IN GLIOBLASTOMA USING HISTOPATHOLOGY VIA AN END-TO-END DEEP LEARNING PIPELINE: A LARGE MULTI-COHORT STUDY. Neuro-Oncology, 23(Supplement_6), vi143–vi143. https://doi.org/10.1093/NEUONC/NOAB196.558 DOI: https://doi.org/10.1093/neuonc/noab196.558
Zhang, X., Wang, X., Shivashankar, G. V., & Uhler, C. (2022). Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease. Nature Communications, 13(1), 1–17. https://doi.org/10.1038/S41467-022-35233-1;TECHMETA DOI: https://doi.org/10.1038/s41467-022-35233-1
Zhang, X., & Wang, Z. (2019). Multi-modal deep learning identifies glioblastoma biomarkers. PMC Central.
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