Interpretable Machine Learning and Biomarker Discovery for Cancer Risk Assessment
DOI:
https://doi.org/10.66238/fsrma63Keywords:
Interpretable Machine Learning, Biomarker Discovery, Cancer Risk Assessment, Clinical Decision SupportAbstract
The intersection of artificial intelligence and oncology has ushered in a transformative era for predictive medicine, particularly in the domain of cancer risk assessment. Despite the high predictive accuracy of modern machine learning algorithms, their clinical adoption is frequently impeded by their opaque, black-box nature. This paper provides a comprehensive examination of interpretable machine learning methodologies applied to the discovery of novel biomarkers and the subsequent evaluation of oncological risk. By prioritizing models that offer both high fidelity and clinical transparency, researchers can bridge the gap between computational complexity and medical utility. We systematically explore the theoretical foundations of interpretability, tracing its evolution from simplistic linear approximations to advanced model-agnostic frameworks such as Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations. Furthermore, we discuss the methodological pipelines required to process multi-omics datasets, highlighting how transparent algorithms successfully identify latent biological features indicative of early-stage malignancy. The integration of interpretable systems not only facilitates robust biomarker identification but also ensures compliance with emerging regulatory standards for medical software. Through a detailed analysis of contemporary approaches, this study demonstrates that interpretability does not inherently compromise predictive power. Ultimately, the deployment of transparent machine learning frameworks provides clinicians with actionable, biologically plausible insights, thereby enhancing diagnostic precision, optimizing therapeutic interventions, and advancing the overarching goals of personalized medicine.
References
1. Ren, Y., Wu, D., Tong, Y., Lòpez-De Fede, A., & Gareau, S. (2022, June). Improving Prediction and Risk Factor Analysis of Low Birthweight Baby Outcomes in a US Hospital System. In 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI) (pp. 659-663). IEEE.
2. Wang, Z., Zhang, H., Zhu, D., & Xie, C. (2026). Interpretable and Lightweight Predictive Modeling for Congestive Heart Failure Using ICU Electronic Health Records. Available at SSRN 6412978.
3. Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., et al. (2020). Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge. Nature Medicine, 26(9), 1544–1551.
4. Ren, Y., Wu, D., Khurana, A., Mastorakos, G., Fu, S., Zong, N., ... & Huang, M. (2023, June). Classification of patient portal messages with BERT-based language models. In 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI) (pp. 176-182). IEEE.
5. Zhu, D., Xie, C., Wang, Z., & Zhang, H. (2025). RaX-Crash: A Resource Efficient and Explainable Small Model Pipeline with an Application to City Scale Injury Severity Prediction. arXiv preprint arXiv:2512.07848.
6. Zou, Y., & Yin, Z. (2025). Alignment, mining and fusion: Representation alignment with hard negative mining and selective knowledge fusion for medical visual question answering. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 29623-29633).
7. Ren, Y., Wu, D., & Lopez–De Fede, A. (2022, June). Identification and Prediction of Low-Birthweight Baby Outcomes and Mom Risk Factors. In 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI) (pp. 01-02). IEEE.
8. Shan, T., Lalor, E. C., & Maddox, R. K. (2026). Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding. Journal of Neuroscience, 46(4).
9. Peng, Y., Tang, T., Li, Q., Zhou, S., Sun, Q., Zhou, X., ... & Chen, Y. (2024). Mycobacterium tuberculosis FadD18 Promotes Proinflammatory Cytokine secretion to inhibit the intracellular survival of Bacillus Calmette–Guérin. Cells, 13(12), 1019.
10. Guo, Y., Hutabarat, Y., Owaki, D., & Hayashibe, M. (2023). Speed-variable gait phase estimation during ambulation via temporal convolutional network. IEEE Sensors Journal, 24(4), 5224-5236.
11. Zou, Y., & Yin, Z. (2025). MVCM: Enhancing Multi-View and Cross-Modality Alignment for Medical Visual Question Answering and Medical Image-Text Retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 180-190).
12. Zeng, Y., & Li, C. (2026). Overcoming Data Scarcity in Pediatric Medicine: Federated Learning for Multi-institutional Diagnostic Models Without Data Migration. Frontiers in Healthcare Technology, 3(1), 57-68.
13. Cai, L., Pfob, A., Barr, R. G., Duda, V., Alwafai, Z., Balleyguier, C., ... & Golatta, M. (2025). Deep learning model for breast shear wave elastography to improve breast cancer diagnosis (INSPiRED 006): An international, multicenter analysis. Journal of Clinical Oncology, 43(32), 3482-3493.
14. Wu, D., Ng, M., Gupta, S. S., Raynor, P., Tao, Y., Ren, Y., ... & Olatosi, B. (2024). Disclosure patterns of opioid use disorders in perinatal care during the opioid epidemic on X from 2019 to 2021: Thematic analysis. JMIR Pediatrics and Parenting, 7(1), e52735.
15. Wei, D., Lin, X., & Bresalier, R. S. (2025). Reframing cancer cachexia: the vagal brain–liver axis as a novel neuro-metabolic target. Signal Transduction and Targeted Therapy, 10(1), 370.
16. Li, R., Wong, S. Y., Duan, B., & Kahn, S. M. (2025). A gamified metaverse platform (Mammoverse) for breast health education: A mixed-methods study. Digital Health, 11, 20552076251390466.
17. Lin, Y., Xue, B., Zhang, M., Schofield, S., & Green, R. (2025, November). Genetic Algorithms For Parameter Optimization for Disparity Map Generation of Radiata Pine Branch Images. In 2025 40th International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 1-6). IEEE.
18. Lin, X., Twelkmeyer, T., Wang, S. Y., Xu, R. N., Wang, F. S., Zhang, C., & Tang, H. (2020). An immunopathogenic perspective of interleukin-1 signaling. Cellular & Molecular Immunology, 17(8), 892-893.
19. Zeng, D., Yang, Y., Tang, Y., Zhao, L., Wang, X., Yun, D., ... & Lin, H. (2025). Shaping school for childhood myopia: the association between floor area ratio of school environment and myopia in China. British Journal of Ophthalmology, 109(1), 146-151.
20. Ma, Y., Qu, D., & Wang, Y. (2026). Dynamic community detection using class preserving time series generation with Fourier Markov diffusion. Scientific Reports.（https://doi.org/10.1038/s41598-026-37699-1）
21. Kelley, D. R., Snoek, J., & Rinn, J. L. (2016). Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Research, 26(7), 990–999.
22. Zhang, W., Xu, Y., Zhao, F., Tarbe, M., Zhou, S., Wang, W., ... & Tang, H. (2020). The pre-existing cellular immunity to Japanese encephalitis virus heterotypically protects mice from Zika virus infection. Science Bulletin, 65(5), 402-409.
23. Ren, Y., Wu, D., Tong, Y., López-DeFede, A., & Gareau, S. (2023). Issue of data imbalance on low birthweight baby outcomes prediction and associated risk factors identification: establishment of benchmarking key machine learning models with data rebalancing strategies. Journal of medical Internet research, 25, e44081.
24. Lin, X., Twelkmeyer, T., Zhu, D., Zhang, L., Zhao, Y., Zhang, C., ... & Tang, H. (2021). Homeostatic regulation of T follicular helper and antibody response to particle antigens by IL-1Ra of medullary sinus macrophage origin. Proceedings of the National Academy of Sciences, 118(17), e2019798118.
Ahmad, N. R. (2025). Monetary policy, inflation and unemployment: The interactions and their economic impacts in Pakistan. Pakistan Journal of Economics and Public Policy, 5(1), 66–81.
Ahmad, N. R. (2025). Leadership ethics in practice: Employees’ experiences of integrity and hypocrisy in public-sector organizations in Pakistan. Journal of Applied Leadership and Transparency. https://doi.org/10.63878/jalt874
Toor, M. J., Shabbir, S., Rauf, A., Nisar, A., Safdar, S., & Zaib-Un-Nisa. (n.d.). The role of structured-communication-technique in developing pragmatic language capability among ASD-children. https://doi.org/10.63878/cjssr.v3i2.785
Toor, M. J., Mehmood, H., Yasmeen, A., Honey, Q., Waseem, M., & Wasif, M. (n.d.). Exploring the effect of verbal encouraging phrases (VEP) on Chinese language learners’ academic achievement at university level.
Toor, M. J., Arafat, M., Ayoub, M., Ali, M., & Alessi Longa, F. E. (n.d.). From coherent self to fragmented persona: Digital identity fragmentation and academic self-concept among Generation Z, with comparative insights from Generation Alpha. https://doi.org/10.63878/cjssr.v4i2.2318
Katsitadze, N., Kharadze, N., & Pirtskhalaishvili, D. (2025). The impact of social media on students’ effective time management. Baltic Journal of Economic Studies, 11(1), 200–210. https://doi.org/10.30525/2256-0742/2025-11-1-200-210
Toor, M. J., Zafar, S., Omer, F., Sarwar, M., & Hussain, A. K. (n.d.). Teachers’ perspectives on effectiveness of translanguaging in enhancing English language learning at secondary school level.
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