From Multi-Omics Prediction to Clinical Workflow: An Interoperable, Bias-Audited, Human-in-the-Loop Decision-Support Framework for Precision Oncology

References

[1] Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J.C. and Stegle, O. (2020) 'MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data', Genome Biology, 21, 111. doi: 10.1186/s13059-020-02015-1.

[2] Campbell, J.D., Alexandrov, A., Kim, J., Wala, J., Berger, A.H., Pedamallu, C.S. et al. (2018) 'Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas', Cell Reports, 23(1), pp. 184-201. doi: 10.1016/j.celrep.2018.03.033.

[3] Cao, H. et al. (2024) 'wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning', British Journal of Cancer, 130(6), pp. 1001-1012. doi: 10.1038/s41416-024-02587-w.

[4] Chingezi, E., Chingezi, L., Ganyani, L., Yelduora, P.G. and Mupa, M.N. (2026) 'Reducing repeat findings and control failures in operationally intensive U.S. enterprises: A data-driven internal audit framework for remediation, loss prevention, and governance improvement', World Journal of Advanced Research and Reviews, 30(03), pp. 2188-2199. doi: 10.30574/wjarr.2026.30.3.1792.

[5] Chingezi, E., Chingezi, L., Ganyani, L., Yelduora, P.G. and Mupa, M.N. (2026) 'Accounting analytics for inventory integrity and working capital control: Continuous auditing approaches for ERP-enabled supply chains', Iconic Research and Engineering Journals, 10(1). doi: 10.64388/IREV10I1-1719387.

[6] Choga, C.R. (2026a) Integrating DNA and RNA Multi-Omics for Improved Survival Prediction in Lung Cancer. Master's thesis, Department of Mathematics, Yeshiva University.

[7] Choga, C.R. (2026b) Clinical Prognosis Report: Patient TCGA-S2-AA1A. Prototype patient-level report generated from the multi-omics transformer pipeline.

[8] Choga, C.R. and Yu, S. (2026) A Transformer-Based Multi-Omics Clinical Decision Support System for Genomic Risk Stratification in Lung Adenocarcinoma. Research manuscript.

[9] Collins, G.S., Dhiman, P., Andaur Navarro, C.L., Ma, J., Hooft, L., Reitsma, J.B., Logullo, P. and Moons, K.G.M. (2024) 'TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods', BMJ, 385, e078378. doi: 10.1136/bmj-2023-078378.

[10] Cox, D.R. (1972) 'Regression models and life-tables', Journal of the Royal Statistical Society: Series B, 34(2), pp. 187-220.

[11] FDA (2022) Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff. Silver Spring, MD: U.S. Food and Drug Administration.

[12] FDA, Health Canada and MHRA (2021) Good Machine Learning Practice for Medical Device Development: Guiding Principles. Silver Spring/Ottawa/London: FDA, Health Canada and MHRA.

[13] HL7 (2026a) US Core Implementation Guide, Version 9.0.0. Ann Arbor, MI: Health Level Seven International. Available at: https://hl7.org/fhir/us/core/ (Accessed: 30 July 2026).

[14] HL7 (2026b) FHIR Genomics. Ann Arbor, MI: Health Level Seven International. Available at: https://fhir.hl7.org/fhir/genomics.html (Accessed: 30 July 2026).

[15] Homwe, T., Mupa, M.N., Matope, A., Mlambo, N., Chingezi, L. and Chihota, T.A. (2025) 'Interpretable machine learning for audit planning: Improving misstatement and compliance risk detection in financial services', World Journal of Advanced Research and Reviews, 28(02), pp. 925-933. doi: 10.30574/wjarr.2025.28.2.3779.

[16] Hove, T.L., Kabera, S.T., Midzi, L.A., Kufandada, D., Nguruve, T., Adu-Gyamfi, R.K. and Mupa, M.N. (2026) 'Explainable multi-biomarker machine learning for prostate cancer risk stratification', World Journal of Advanced Research and Reviews, 30(01), pp. 2614-2623. doi: 10.30574/wjarr.2026.30.1.1152.

[17] Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T. and Kluger, Y. (2018) 'DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network', BMC Medical Research Methodology, 18, 24. doi: 10.1186/s12874-018-0482-1.

[18] Liu, Y. et al. (2025) 'Clinical prediction models using machine learning in oncology: challenges and recommendations', BMJ Oncology, 4(1), e000914.

[19] Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I.D. and Gebru, T. (2019) 'Model cards for model reporting', Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 220-229.

[20] Mo, Q., Wang, S., Seshan, V.E., Olshen, A.B., Schultz, N., Sander, C., Powers, R.S., Ladanyi, M. and Shen, R. (2013) 'iClusterPlus: integrative clustering of multiple genomic data types', Nature Methods, 10(7), pp. 679-684. doi: 10.1038/nmeth.2651.

[21] Nahin333 (n.d.) TCGA-LUAD. Kaggle dataset. Available at: https://www.kaggle.com/datasets/nahin333/tcgaluad (Accessed: 30 July 2026).

[22] Obermeyer, Z., Powers, B., Vogeli, C. and Mullainathan, S. (2019) 'Dissecting racial bias in an algorithm used to manage the health of populations', Science, 366(6464), pp. 447-453. doi: 10.1126/science.aax2342.

[23] Okebu, D.T.C. and Mupa, M.N. (2026a) 'Preventing the unpredictable: An integrated human factors approach to reducing aviation accidents in the era of advanced flight technology', Iconic Research and Engineering Journals, 9(12), pp. 3168-3181. doi: 10.64388/IREV9I12-1719259.

[24] Okebu, D.T.C. and Mupa, M.N. (2026b) 'Predictive training analytics for competency-based pilot instruction: Early detection of checkride risk, procedural noncompliance and safety-relevant learning gaps', World Journal of Advanced Research and Reviews, 30(03), pp. 1928-1942. doi: 10.30574/wjarr.2026.30.3.1774.

[25] ONC (2024) Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing Final Rule. Washington, DC: Office of the National Coordinator for Health Information Technology.

[26] Rappoport, N. and Shamir, R. (2019) 'Multi-omic and multi-view clustering algorithms: review and cancer benchmark', Genome Biology, 20, 1-18. doi: 10.1186/s13059-019-1862-5.

[27] Sundararajan, M., Taly, A. and Yan, Q. (2017) 'Axiomatic attribution for deep networks', Proceedings of the 34th International Conference on Machine Learning, pp. 3319-3328.

[28] The Cancer Genome Atlas Research Network (2014) 'Comprehensive molecular profiling of lung adenocarcinoma', Nature, 511(7511), pp. 543-550. doi: 10.1038/nature13385.

[29] Van Calster, B., McLernon, D.J., van Smeden, M., Wynants, L. and Steyerberg, E.W. (2019) 'Calibration: the Achilles heel of predictive analytics', BMC Medicine, 17, 230. doi: 10.1186/s12916-019-1466-7.

[30] Vickers, A.J. and Elkin, E.B. (2006) 'Decision curve analysis: a novel method for evaluating prediction models', Medical Decision Making, 26(6), pp. 565-574. doi: 10.1177/0272989X06295361.

[31] Waalbannyantudre (n.d.) Gene Expression Cancer RNA-Seq. Kaggle dataset. Available at: https://www.kaggle.com/datasets/waalbannyantudre/gene-expression-cancer-rna-seq-donated-on-682016 (Accessed: 30 July 2026).

[32] Zehir, A., Benayed, R., Shah, R.H., Syed, A., Middha, S., Kim, H.R. et al. (2017) 'Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients', Nature Medicine, 23, pp. 703-713. doi: 10.1038/nm.4333.

[33] Zhang, J., Zhang, X., Zhang, Z., Wang, Z. and Wang, W. (2021) 'Deep learning-based multi-omics integration for survival prediction in cancer', Nature Machine Intelligence, 3(12), pp. 1039-1048. doi: 10.1038/s42256-021-00427-8.

@article{1722516,
      author = {Cleopas Russell Choga, Manyara Sandra Kasanhayi, Nkosana Mkandla, Marlon Munjoma, Munashe Naphtali Mupa},
      title = {From Multi-Omics Prediction to Clinical Workflow: An Interoperable, Bias-Audited, Human-in-the-Loop Decision-Support Framework for Precision Oncology},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {2741-2757},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722516.pdf},
      abstract = {Multi-omics survival models for precision oncology are increasingly capable of producing patient-level risk estimates, subtype projections and molecular explanations. Yet a model that performs adequately in retrospective validation is not automatically ready for clinical use. The translational problem is broader than model architecture: oncology teams must determine whether genomic risk predictions can be exchanged through interoperable health information systems, audited for subgroup bias, interpreted by clinicians, monitored for drift and governed as decision support rather than autonomous diagnosis. This article develops an interoperable, bias-audited and human-in-the-loop decision-support framework for multi-omics precision oncology, using lung adenocarcinoma (LUAD) as the applied case. The empirical basis combines four evidence layers: a TCGA-LUAD and MSK-IMPACT transformer manuscript, a DNA/RNA multi-omics survival thesis, an individualized LUAD patient report, and a reproducible secondary-data design linked to public TCGA/Kaggle and cBioPortal data pathways. The attached research record shows that a ridge RNA+clinical baseline achieved the strongest discrimination (C-index = 0.724), while the full DNA/RNA multi-omics model achieved lower aggregate discrimination (C-index = 0.682) but improved biological interpretability, temporal stability and clinical-decision value. The transformer system achieved moderate internal discrimination and modest external transportability, while still producing meaningful risk ordering and Integrated Gradients explanations. A simulated silent-mode workflow analysis then demonstrates how a standards-based clinical implementation layer can reduce review burden, improve missing-data controls, strengthen override documentation and surface subgroup-specific calibration risk before any prospective deployment. The paper argues that precision-oncology AI should be evaluated not only by C-index, but by an integrated evidence package: discrimination, calibration, decision utility, subgroup fairness, interoperability, clinician usability, audit logging, drift monitoring and accountable human oversight.},
      keywords = {precision oncology; multi-omics; clinical decision support; FHIR; human-in-the-loop AI; bias audit; lung adenocarcinoma; genomic risk stratification; model governance; Integrated Gradients JEL/MSC/MeSH-style classification: clinical decision support systems; machine learning; genomics; oncology; biomedical informatics; survival analysis; risk assessment},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722516}
  }