Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer
Open accessAbstract
Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e. multi-modal personalized biomarkers). Identifying such modules would not only predict disease but also provide effective therapeutic drug target information for individual patients. To solve this problem, we developed a novel model (denoted multi-modal personalized dynamic network biomarkers (MMPDNB)) based on a multi-modal optimization mechanism and personalized dynamic network biomarker (PDNB) theory, which can provide multiple modules of personalized biomarkers and unveil their multi-modal properties. Using the genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, we validated the effectiveness of the MMPDNB model. The experimental results showed that compared with other advanced methods, MMPDNB can more effectively predict the critical state with the highest early warning signal score during cancer development. Furthermore, MMPDNB more significantly identified PDNBs containing driver and biomarker genes specific to cancer tissues. More importantly, we validated the biological significance of multi-modal PDNBs, which could provide effective drug targets of individual patients as well as markers for predicting early warning signals of the critical disease state. In conclusion, multi-modal optimization is an effective method to identify PDNBs and offers a new perspective for understanding tumor heterogeneity in cancer precision medicine.
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Nontextual data types play a crucial role in diagnosis, effective treatment planning, research, and patient care.
“Moreover, nontextual data types play a crucial role in diagnosis, effective treatment planning, research, and patient care [9-11].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [10]
The Introduction (page unavailable) describes using patients’ genomic data to predict early warning signals of cancer, and the Biologic significance section (page unavailable) identifies potential drug targets for early intervention. This supports a narrower point about nontextual molecular data in cancer prediction and treatment research, but not the broad claim about nontextual data types across diagnosis, effective treatment planning, research, and patient care. The paper’s “multi-modal” refers to alternative biomarker configurations, not different data types. (LLM, conf=0.83)
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Cited by
Sorted by citations
Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
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- Authors
- Liang, J., Li, Z.-W., Yue, C.-T., Hu, Z., Cheng, H., Liu, Z.-X., & Guo, W.-F.
- Affiliations
- Zhengzhou University (CN)
- School of Electrical Engineering, Zhengzhou University , Zhengzhou 450001, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center , Guangzhou 510060, China
- Corresponding author
- Guo, Wei-Feng
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-07-21
- Volume
- 23
- Issue
- 5
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W4285987711
- Cited by
- 23
- References
- 57
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 6 · 2023: 4
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Briefings in Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (62002329, 61922072, 61876169, 62106230)
- Key Scientific and Technological Projects of Henan Province (212102310083, 212102210510)
- China Postdoctoral Science Foundation (2021M692915, 2021T140616, 2021M692920)
- Henan Postdoctoral Foundation (202002021)
- Research start-up funds for top doctors in Zhengzhou University (32211739, 202003019)
- State Key Laboratory of Oncology in South China (HN2021-01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- personalized biomarkers, multimodal optimization, disease prediction, breast cancer, lung cancer, driver genes, therapeutic drug targets, tumor heterogeneity, precision medicine, network biomarkers
- MeSH terms
- Humans, Lung Neoplasms, Biomarkers, Genomics, Precision Medicine
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