Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A deep learning and large language hybrid workflow for omics interpretation

Nature Biomedical Engineering (2026)Cite this article

Subjects

Abstract

Profiling molecular panorama from massive omics data identifies regulatory networks in cells but requires mechanistic interpretation and experimental follow up. Here we combine deep learning and large language model reasoning to develop a hybrid workflow for omics interpretation, called LyMOI. LyMOI incorporates GPT-3.5 for biological knowledge reasoning and a large graph model with graph convolutional networks (GCNs). The large graph model integrates evolutionarily conserved protein interactions and uses hierarchical fine-tuning to predict context-specific molecular regulators from multi-omics data. GPT-3.5 then generates machine chain-of-thought (CoT) to mechanistically interpret their roles in biological systems. Focusing on autophagy, LyMOI mechanistically interprets 1.3 TB transcriptomic, proteomic and phosphoproteomic data and expands the knowledge of autophagy regulators. We also show that LyMOI highlights two human oncoproteins, CTSL and FAM98A, for enhancing autophagy upon treatment with disulfiram (DSF), an antitumour agent. Silencing these genes in vitro attenuates DSF-mediated autophagy and suppresses cancer cell proliferation. Strikingly, DSF treatment with Z-FY-CHO, a CTSL-specific inhibitor previously used for preventing SARS-CoV-2 infection, potently inhibits tumour growth in vivo.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The whole workflow of the hybrid framework.
Fig. 2: Genome-wide interpretation of autophagic processes using LLM.
Fig. 3: Overview of the LyMOI framework and performance evaluation.
Fig. 4: Identification of new players in yeast autophagy.
Fig. 5: FAM98A and CTSL serve as key players in DSF-mediated activation of autophagy.
Fig. 6: CTSL and FAM98A promote cancer survival via the autophagy pathway.
Fig. 7: Combination therapy and extended application of LyMOI.

Similar content being viewed by others

Data availability

The data supporting the results in this study are available within the paper and its Supplementary Information. The RNA-seq data of yeast were deposited into the NCBI Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra) with the dataset identifier PRJNA912308 (ref. 90). The raw MS datasets of the proteome and phosphoproteome of yeast were submitted to integrated proteome resources (iProX, http://www.iprox.org/) with the dataset identifier PXD038804 (ref. 91). Source data are provided with this paper.

Code availability

The source code for LyMOI in this study is available on GitHub (https://github.com/BioCUCKOO/LyMOI)92.

References

  1. Stefely, J. A. et al. Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling. Nat. Biotechnol. 34, 1191–1197 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Karczewski, K. J. & Snyder, M. P. Integrative omics for health and disease. Nat. Rev. Genet. 19, 299–310 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rhodes, D. R. & Chinnaiyan, A. M. Integrative analysis of the cancer transcriptome. Nat. Genet. 37, S31–S37 (2005).

    Article  CAS  PubMed  Google Scholar 

  4. Chung, M. et al. Best practices on the differential expression analysis of multi-species RNA-seq. Genome Biol. 22, 121 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yamada, R. et al. Interpretation of omics data analyses. J. Hum. Genet. 66, 93–102 (2021).

    Article  PubMed  Google Scholar 

  6. Subramanian, I. et al. Multi-omics data integration, interpretation, and its application. Bioinform. Biol. Insights 14, 1177932219899051 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Shu, T. et al. Plasma proteomics identify biomarkers and pathogenesis of COVID-19. Immunity 53, 1108–1122.e5 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Shui, K. et al. Small-sample learning reveals propionylation in determining global protein homeostasis. Nat. Commun. 14, 2813 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Yuan, Y. et al. PIM1 promotes hepatic conversion by suppressing reprogramming-induced ferroptosis and cell cycle arrest. Nat. Commun. 13, 5237 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hirschberg, J. & Manning, C. D. Advances in natural language processing. Science 349, 261–266 (2015).

    Article  CAS  PubMed  Google Scholar 

  11. ChatGPT: Optimizing Language Models for Dialogue (OpenAI, 2022).

  12. Christiano, P. F. et al. Deep reinforcement learning from human preferences. In Proc. 31st International Conference on Neural Information Processing Systems (eds von Luxburg, U. et al.) 4302–4310 (Curran, 2017).

  13. Brown, T. B. et al. Language models are few-shot learners. In Proc. 34th Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) 1–25 (2020).

  14. Wei, J. S. et al. Chain-of-thought prompting elicits reasoning in large language models. In Proc. 36th International Conference on Neural Information Processing Systems (eds Koyejo, S. et al.) 24824–24837 (Curran, 2022).

  15. Klionsky, D. J. et al. Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition). Autophagy 17, 1–382 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ulgherait, M. et al. Circadian autophagy drives iTRF-mediated longevity. Nature 598, 353–358 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Skrott, Z. et al. Alcohol-abuse drug disulfiram targets cancer via p97 segregase adaptor NPL4. Nature 552, 194–199 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhao, M.-M. et al. Novel cleavage sites identified in SARS-CoV-2 spike protein reveal mechanism for cathepsin L-facilitated viral infection and treatment strategies. Cancer Discov. 8, 53 (2022).

    CAS  Google Scholar 

  19. Podgorski, J. & Berg, M. Global threat of arsenic in groundwater. Science 368, 845–850 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Diamantopoulou, Z. et al. The metastatic spread of breast cancer accelerates during sleep. Nature 607, 156–162 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Obradović, M. M. S. et al. Glucocorticoids promote breast cancer metastasis. Nature 567, 540–544 (2019).

    Article  PubMed  Google Scholar 

  22. Hirota, T. & King, B. H. Autism spectrum disorder: a review. JAMA 329, 157–168 (2023).

    Article  CAS  PubMed  Google Scholar 

  23. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e21 (2022).

    Article  CAS  PubMed  Google Scholar 

  24. Tang, F. et al. A pan-cancer single-cell panorama of human natural killer cells. Cell 186, 4235–4251.e20 (2023).

    Article  CAS  PubMed  Google Scholar 

  25. Velmeshev, D. et al. Single-cell analysis of prenatal and postnatal human cortical development. Science 382, eadf0834 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Deng, W. et al. THANATOS: an integrative data resource of proteins and post-translational modifications in the regulation of autophagy. Autophagy 14, 296–310 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Han, Z. et al. Model-based analysis uncovers mutations altering autophagy selectivity in human cancer. Nat. Commun. 12, 3258 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Santos, A. et al. A knowledge graph to interpret clinical proteomics data. Nat. Biotechnol. 40, 692–702 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhang, Z. et al. Large graph models: a perspective. Preprint at https://doi.org/10.48550/arXiv.2308.14522 (2023).

  30. Yu, H. et al. Annotation transfer between genomes: protein–protein interologs and protein–DNA regulogs. Genome Res. 14, 1107–1118 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lyu, Y., Huang, X. & Zhang, Z. Revisiting 2D convolutional neural networks for graph-based applications. IEEE Trans. Pattern Anal. Mach. Intell. 45, 6909–6922 (2023).

    Article  PubMed  Google Scholar 

  32. Díez, J., Walter, D., Muñoz-Pinedo, C. & Gabaldón, T. DeathBase: a database on structure, evolution and function of proteins involved in apoptosis and other forms of cell death. Cell Death Differ. 17, 735–736 (2010).

    Article  PubMed  Google Scholar 

  33. Homma, K., Suzuki, K. & Sugawara, H. The Autophagy Database: an all-inclusive information resource on autophagy that provides nourishment for research. Nucleic Acids Res. 39, D986–D990 (2011).

    Article  CAS  PubMed  Google Scholar 

  34. Moussay, E. et al. The acquisition of resistance to TNFα in breast cancer cells is associated with constitutive activation of autophagy as revealed by a transcriptome analysis using a custom microarray. Autophagy 7, 760–770 (2011).

    Article  CAS  PubMed  Google Scholar 

  35. Xu, J. & Li, Y. H. miRDeathDB: a database bridging microRNAs and the programmed cell death. Cell Death Differ. 19, 1571 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Arntzen, M., Bull, V. H. & Thiede, B. Cell death proteomics database: consolidating proteomics data on cell death. J. Proteome Res. 12, 2206–2213 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Wanichthanarak, K., Cvijovic, M., Molt, A. & Petranovic, D. yApoptosis: yeast apoptosis database. Database 2013, bat068 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Türei, D. et al. Autophagy Regulatory Network—a systems-level bioinformatics resource for studying the mechanism and regulation of autophagy. Autophagy 11, 155–165 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wu, D. et al. ncRDeathDB: a comprehensive bioinformatics resource for deciphering network organization of the ncRNA-mediated cell death system. Autophagy 11, 1917–1926 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wang, N. N. et al. HAMdb: a database of human autophagy modulators with specific pathway and disease information. J. Cheminform. 10, 34 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Chen, K. et al. Autophagy and Tumor Database: ATdb, a novel database connecting autophagy and tumor. Database https://doi.org/10.1093/database/baaa052 (2020).

  42. Zhou, N. & Bao, J. FerrDb: a manually curated resource for regulators and markers of ferroptosis and ferroptosis–disease associations. Database https://doi.org/10.1093/database/baaa021 (2020).

  43. Zhang, L. et al. MCDB: a comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction. Acta Pharm. Sin. B 11, 3092–3104 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sun, Y. J., Sheng, D. F., Zhou, Z. H. & Wu, Y. F. AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content. Hum. Soc. Sci. Commun. https://doi.org/10.1057/S41599-024-03811-X (2024).

  45. Bang, Y. et al. A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity. Preprint at https://arxiv.org/abs/2302.04023 (2023).

  46. Zhu, W., Swaminathan, G. & Plowey, E. D. GA binding protein augments autophagy via transcriptional activation of BECN1-PIK3C3 complex genes. Autophagy 10, 1622–1636 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Sun, W., Jia, M., Feng, Y. & Cheng, X. Lactate is a bridge linking glycolysis and autophagy through lactylation. Autophagy 19, 3240–3241 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Fujioka, Y. et al. Structural basis of starvation-induced assembly of the autophagy initiation complex. Nat. Struct. Mol. Biol. 21, 513–521 (2014).

    Article  CAS  PubMed  Google Scholar 

  49. Schreiber, A. et al. Multilayered regulation of autophagy by the Atg1 kinase orchestrates spatial and temporal control of autophagosome formation. Mol. Cell 81, 5066–5081.e10 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Feng, Y. et al. Phosphorylation of Atg9 regulates movement to the phagophore assembly site and the rate of autophagosome formation. Autophagy 12, 648–658 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Cowley, M. J. et al. PINA v2.0: mining interactome modules. Nucleic Acids Res. 40, D862–D865 (2012).

    Article  CAS  PubMed  Google Scholar 

  52. Das, J. & Yu, H. HINT: high-quality protein interactomes and their applications in understanding human disease. BMC Syst. Biol. 6, 92 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Razick, S., Magklaras, G. & Donaldson, I. M. iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9, 405 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Oughtred, R. et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 47, D529–D541 (2019).

    Article  CAS  PubMed  Google Scholar 

  55. Calderone, A., Castagnoli, L. & Cesareni, G. mentha: a resource for browsing integrated protein-interaction networks. Nat. Methods 10, 690–691 (2013).

    Article  CAS  PubMed  Google Scholar 

  56. Kotlyar, M., Pastrello, C., Malik, Z. & Jurisica, I. IID 2018 update: context-specific physical protein–protein interactions in human, model organisms and domesticated species. Nucleic Acids Res. 47, D581–D589 (2019).

    Article  CAS  PubMed  Google Scholar 

  57. Li, T. et al. A scored human protein–protein interaction network to catalyze genomic interpretation. Nat. Methods 14, 61–64 (2017).

    Article  CAS  PubMed  Google Scholar 

  58. Galluzzi, L. et al. Molecular definitions of autophagy and related processes. EMBO J. 36, 1811–1836 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Yi, C. et al. Formation of a Snf1-Mec1-Atg1 module on mitochondria governs energy deprivation-induced autophagy by regulating mitochondrial respiration. Dev. Cell 41, 59–71.e54 (2017).

    Article  CAS  PubMed  Google Scholar 

  60. Yi, C., Tong, J. J. & Yu, L. Mitochondria: the hub of energy deprivation-induced autophagy. Autophagy 14, 1084–1085 (2018).

    CAS  PubMed  Google Scholar 

  61. Clement, S. T., Dixit, G. & Dohlman, H. G. Regulation of yeast G protein signaling by the kinases that activate the AMPK homolog Snf1. Sci. Signal. 6, ra78 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Mok, J. et al. Deciphering protein kinase specificity through large-scale analysis of yeast phosphorylation site motifs. Sci. Signal. 3, ra12 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Asano, S. et al. Direct phosphorylation and activation of a Nim1-related kinase Gin4 by Elm1 in budding yeast. J. Biol. Chem. 281, 27090–27098 (2006).

    Article  CAS  PubMed  Google Scholar 

  64. Hu, Y. et al. The disulfiram/copper complex induces autophagic cell death in colorectal cancer by targeting ULK1. Front. Pharmacol. 12, 752825 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Jivan, R. et al. Disulfiram with or without metformin inhibits oesophageal squamous cell carcinoma in vivo. Cancer Lett. 417, 1–10 (2018).

    Article  CAS  PubMed  Google Scholar 

  66. Wu, X. et al. Suppressing autophagy enhances disulfiram/copper-induced apoptosis in non-small cell lung cancer. Eur. J. Pharmacol. 827, 1–12 (2018).

    Article  CAS  PubMed  Google Scholar 

  67. Xu, S. et al. Inhibition of cathepsin L alleviates the microglia-mediated neuroinflammatory responses through caspase-8 and NF-κB pathways. Neurobiol. Aging 62, 159–167 (2018).

    Article  CAS  PubMed  Google Scholar 

  68. Liu, H. et al. Oxidized DJ-1 activates the p-IKK/NF-κB/Beclin1 pathway by binding PTEN to induce autophagy and exacerbate myocardial ischemia-reperfusion injury. Eur. J. Pharmacol. 971, 176496 (2024).

    Article  CAS  PubMed  Google Scholar 

  69. Tate, J. G. et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. 47, D941–D947 (2019).

    Article  CAS  PubMed  Google Scholar 

  70. Kenig, S., Frangež, R., Pucer, A. & Lah, T. Inhibition of cathepsin L lowers the apoptotic threshold of glioblastoma cells by up-regulating p53 and transcription of caspases 3 and 7. Apoptosis 16, 671–682 (2011).

    Article  CAS  PubMed  Google Scholar 

  71. Zhao, M. M. et al. Cathepsin L plays a key role in SARS-CoV-2 infection in humans and humanized mice and is a promising target for new drug development. Signal Transduct. Target. Ther. https://doi.org/10.1038/s41392-021-00558-8 (2021).

  72. Sudhan, D. R., Pampo, C., Rice, L. & Siemann, D. W. Cathepsin L inactivation leads to multimodal inhibition of prostate cancer cell dissemination in a preclinical bone metastasis model. Int. J. Cancer 138, 2665–2677 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Richard, V. et al. The double agents in liquid biopsy: promoter and informant biomarkers of early metastases in breast cancer. Mol. Cancer 21, 95 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Xu, J. et al. ATP11B inhibits breast cancer metastasis in a mouse model by suppressing externalization of nonapoptotic phosphatidylserine. J. Clin. Invest. https://doi.org/10.1172/jci149473 (2022).

  75. Jiang, C. C. et al. Signalling pathways in autism spectrum disorder: mechanisms and therapeutic implications. Signal Transduct. Target. Ther. 7, 229 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Bheda, A., Creek, K. E. & Pirisi, L. Loss of p53 induces epidermal growth factor receptor promoter activity in normal human keratinocytes. Oncogene 27, 4315–4323 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Linder, M. et al. EGFR is required for FOS-dependent bone tumor development via RSK2/CREB signaling. EMBO Mol. Med. https://doi.org/10.15252/emmm.201809408 (2018).

  78. Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. 41, 1099–1106 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Yang, F. et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat. Mach. Intell. 4, 852 (2022).

    Article  Google Scholar 

  81. Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Cui, H. T. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods 21, 1470–1480 (2024).

    Article  CAS  PubMed  Google Scholar 

  83. Wu, A. et al. Causality for large language models. Preprint at https://arxiv.org/abs/2410.15319 (2024).

  84. Lee, S. et al. Reasoning abilities of large language models: in-depth analysis on the abstraction and reasoning corpus. Preprint at https://arxiv.org/abs/2403.11793 (2024).

  85. Kipf, T. N. & Welling, M. Variational graph auto-encoders. Preprint at https://arxiv.org/abs/1611.07308 (2016).

  86. Wu, Z. H. et al. A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 4–24 (2021).

    Article  PubMed  Google Scholar 

  87. Miao, Z., Humphreys, B. D., McMahon, A. P. & Kim, J. Multi-omics integration in the age of million single-cell data. Nat. Rev. Nephrol. 17, 710–724 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Ma, A. et al. Integrative methods and practical challenges for single-cell multi-omics. Trends Biotechnol. 38, 1007–1022 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Zhang, Y. et al. DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae. Autophagy 16, 626–640 (2020).

    Article  CAS  PubMed  Google Scholar 

  90. Tang, D., Zhang, C., Peng, D. & Xue, Y. Transcriptome of Saccharomyces cerevisiae during glucose starvation. Datasets. SRA https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA912308 (2025).

  91. Tang, D., Zhang, C., Peng, D. & Xue, Y. The proteome and phosphoproteome of Saccharomyces cerevisiae during glucose starvation. Datasets. iProX https://www.iprox.cn//page/project.html?id=IPX0005607000 (2025).

  92. Tang, D., Zhang, C., Peng, D. & Xue, Y. LyMOI: large hybrid models for omics interpretation. Source code. GitHub https://github.com/BioCUCKOO/LyMOI (2025).

Download references

Acknowledgements

We thank L. Yang for reading the manuscript and editing the abstract, and Y. Cui for helpful suggestions on experiments. This work was supported by grants from the Natural Science Foundation of China (32341020 and 32341021 to Y.X., 32571718 to D.P.), the National Key R & D Program of China (2022YFC2704304 and 2021YFF0702000 to Y.X.), the Interdisciplinary Research Program of HUST (2023JCYJ010 and 2024JCYJ013 to Y.X.), the Hubei Province Postdoctoral Outstanding Talent Tracking Support Program (to D.P.), the Natural Science Foundation of Hubei Province of China (JCZRYB202500751 to D.P.), the start-up funding of Hubei Hongshan Laboratory (to Y.X.) and the Research Core Facilities for Life Science (HUST to Y.X.).

Author information

Author notes

  1. These authors contributed equally: Dachao Tang, Chi Zhang, Weizhi Zhang.

Authors and Affiliations

  1. Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Dachao Tang, Chi Zhang, Weizhi Zhang, Leming Xiao, Xinhe Huang, Dan Liu, Shanshan Fu, Miaoying Zhao, Di Peng & Yu Xue

  2. National Clinical Research Center for Gynecology and Obstetrics and Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Funian Lu, Chaoyang Sun & Gang Chen

  3. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Jiangyi Shao & Bin Liu

  4. Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Luoying Zhang

  5. Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu, China

    Da Jia

  6. Faculty of Health Sciences, Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Macau, China

    Han-Ming Shen

  7. Hubei Hongshan Laboratory, Wuhan, China

    Yu Xue

Authors
  1. Dachao Tang
    View author publications

    Search author on:PubMed Google Scholar

  2. Chi Zhang
    View author publications

    Search author on:PubMed Google Scholar

  3. Weizhi Zhang
    View author publications

    Search author on:PubMed Google Scholar

  4. Funian Lu
    View author publications

    Search author on:PubMed Google Scholar

  5. Leming Xiao
    View author publications

    Search author on:PubMed Google Scholar

  6. Xinhe Huang
    View author publications

    Search author on:PubMed Google Scholar

  7. Jiangyi Shao
    View author publications

    Search author on:PubMed Google Scholar

  8. Dan Liu
    View author publications

    Search author on:PubMed Google Scholar

  9. Shanshan Fu
    View author publications

    Search author on:PubMed Google Scholar

  10. Miaoying Zhao
    View author publications

    Search author on:PubMed Google Scholar

  11. Luoying Zhang
    View author publications

    Search author on:PubMed Google Scholar

  12. Da Jia
    View author publications

    Search author on:PubMed Google Scholar

  13. Han-Ming Shen
    View author publications

    Search author on:PubMed Google Scholar

  14. Chaoyang Sun
    View author publications

    Search author on:PubMed Google Scholar

  15. Gang Chen
    View author publications

    Search author on:PubMed Google Scholar

  16. Bin Liu
    View author publications

    Search author on:PubMed Google Scholar

  17. Di Peng
    View author publications

    Search author on:PubMed Google Scholar

  18. Yu Xue
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Y.X. and D.P. initiated the project and oversaw all aspects of the project. C.Z. developed the LyMOI framework, with the help of D.T, D.P., X.H. and W.Z. D.T., C.Z. and D.P. compiled the multi-omics data and carried out data analysis. D.T. and D.P. performed the experiments with the help of D.J., H.-M.S., L.Z., L.X., D.L., S.F., F.L., C.S., J.S., M.Z., B.L. and G.C. Y.X., D.T. and C.Z. wrote the manuscript with input from all authors. All authors reviewed and approved the manuscript for publication.

Corresponding authors

Correspondence to Di Peng or Yu Xue.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information (download PDF )

Supplementary Methods, Figs. 1–7 and References.

Reporting Summary (download PDF )

Peer Review File (download PDF )

Supplementary Data 1 (download ZIP )

Supplementary Tables 1–7 and source data for Supplementary Figs. 1–3.

Supplementary Data 2 (download XLSX )

Statistical source data for Supplementary Figs. 3–6.

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, D., Zhang, C., Zhang, W. et al. A deep learning and large language hybrid workflow for omics interpretation. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-025-01576-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41551-025-01576-5

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing