多组学数据整合与机器学习在脓毒症精准诊疗中的应用、挑战与未来展望
The Application, Challenges and Future Prospects of Multi-Omics Data Integration and Machine Learning in the Precision Diagnosis and Treatment of Sepsis
摘要: 脓毒症是一种由宿主对感染反应失调引起的危及生命的综合征，其高度异质性和复杂的病理生理机制给早期诊断、风险分层和精准治疗带来了巨大挑战。传统的单一生物标志物和临床评分系统难以全面捕捉其分子复杂性。近年来，多组学技术(包括基因组学、转录组学、蛋白质组学、代谢组学)与机器学习算法的结合，为深入解析脓毒症的分子亚型、发现新型生物标志物、构建预测模型以及探索个体化治疗策略开辟了新途径。本文综述了多组学与机器学习在脓毒症及其相关并发症(如脓毒症相关脑病、凝血病、急性肾损伤、急性呼吸窘迫综合征)精准诊疗中的最新应用进展，系统分析了当前面临的数据整合、模型可解释性、临床转化等关键挑战，并对未来发展方向进行了展望。
Abstract: Sepsis is a life-threatening syndrome caused by a dysregulated host response to infection. Its high heterogeneity and complex pathophysiological mechanisms pose significant challenges to early diagnosis, risk stratification, and precise treatment. Traditional single biomarkers and clinical scoring systems are insufficient to fully capture its molecular complexity. In recent years, the integration of multi-omics technologies (including genomics, transcriptomics, proteomics, and metabolomics) with machine learning algorithms has opened up new avenues for in-depth analysis of sepsis molecular subtypes, discovery of novel biomarkers, construction of predictive models, and exploration of individualized treatment strategies. This article reviews the latest application progress of multi-omics and machine learning in the precise diagnosis and treatment of sepsis and its related complications (such as sepsis-associated encephalopathy, coagulopathy, acute kidney injury, and acute respiratory distress syndrome), systematically analyzes the key challenges currently faced, such as data integration, model interpretability, and clinical translation, and looks forward to future development directions.
文章引用：李志君, 朱鹏. 多组学数据整合与机器学习在脓毒症精准诊疗中的应用、挑战与未来展望[J]. 临床医学进展, 2026, 16(3): 2189-2203. https://doi.org/10.12677/acm.2026.1631012

参考文献

[1] Gao, Z. and Xu, Z. (2025) Postoperative Sepsis-Associated Neurocognitive Disorder: Mechanisms, Predictive Strategies, and Treatment Approaches. Frontiers in Medicine, 12, Article 1513833. [Google Scholar] [CrossRef] [PubMed]
[2] Tang, L., Zhang, W., Liao, Y., Wang, W., Wu, Y., Zou, Z., et al. (2025) Decoding Sepsis: Unraveling Key Signaling Pathways for Targeted Therapies. Research, 8, Article ID: 0811. [Google Scholar] [CrossRef] 
[3] Davies, K. and McLaren, J.E. (2024) Destabilisation of T Cell-Dependent Humoral Immunity in Sepsis. Clinical Science, 138, 65-85. [Google Scholar] [CrossRef] [PubMed]
[4] Neu, C., Thiele, Y., Horr, F., Beckers, C., Frank, N., Marx, G., et al. (2022) Damps Released from Proinflammatory Macrophages Induce Inflammation in Cardiomyocytes via Activation of TLR4 and TNFR. International Journal of Molecular Sciences, 23, Article 15522. [Google Scholar] [CrossRef] [PubMed]
[5] He, R., Yue, G., Dong, M., Wang, J. and Cheng, C. (2024) Sepsis Biomarkers: Advancements and Clinical Applications—A Narrative Review. International Journal of Molecular Sciences, 25, Article 9010. [Google Scholar] [CrossRef] [PubMed]
[6] Chuang, C., Yeh, H., Niu, K., Chen, C., Seak, C. and Yen, C. (2025) Diagnostic Performances of Procalcitonin and C-Reactive Protein for Sepsis: A Systematic Review and Meta-Analysis. European Journal of Emergency Medicine, 32, 248-258. [Google Scholar] [CrossRef] [PubMed]
[7] Schupp, T., Weidner, K., Rusnak, J., Jawhar, S., Forner, J., Dulatahu, F., et al. (2023) C-Reactive Protein and Procalcitonin during Course of Sepsis and Septic Shock. Irish Journal of Medical Science (1971-), 193, 457-468. [Google Scholar] [CrossRef] [PubMed]
[8] Bordeanu-Diaconescu, E., Grosu-Bularda, A., Frunza, A., Grama, S., Andrei, M., Neagu, T., et al. (2024) Diagnostic and Prognostic Value of Thrombocytopenia in Severe Burn Injuries. Diagnostics, 14, Article 582. [Google Scholar] [CrossRef] [PubMed]
[9] Li, A., Zhou, Z., Li, D., Sha, P., Hu, H., Lin, Y., et al. (2025) The Molecular Mechanisms of Muscle-Adipose Crosstalk: Myokines, Adipokines, Lipokines and the Mediating Role of Exosomes. Cells, 14, Article 1954. [Google Scholar] [CrossRef] 
[10] Jin, X., Shen, H., Zhou, P., Yang, J., Yang, S., Ni, H., et al. (2025) Research Progress on Sepsis Diagnosis and Monitoring Based on Omics Technologies: A Review. Diagnostics, 15, Article 2887. [Google Scholar] [CrossRef] 
[11] Gong, J., Yang, J., He, Y., Chen, X., Yang, G. and Sun, R. (2022) Construction of m7G Subtype Classification on Heterogeneity of Sepsis. Frontiers in Genetics, 13, Article 1021770. [Google Scholar] [CrossRef] [PubMed]
[12] Ma, C. and Wang, J. (2025) Identification of Glycolysis-Related Signature and Molecular Subtypes in Child Sepsis through Machine Learning and Consensus Clustering: Implications for Diagnosis and Therapeutics. Molecular Biotechnology. [Google Scholar] [CrossRef] [PubMed]
[13] Cheng, T., Xu, Y., Liu, Z., Wang, Y., Zhang, Z. and Huang, W. (2025) Multi-Omics Analysis Reveals Neutrophil Heterogeneity and Key Molecular Drivers in Sepsis-Associated Acute Kidney Injury. Frontiers in Immunology, 16, Article 1637692. [Google Scholar] [CrossRef] 
[14] Luo, X., Hu, H., Sun, Z., Zhang, L. and Li, Y. (2025) Multi-Omics Analysis Reveals That Low Cathepsin S Expression Aggravates Sepsis Progression and Worse Prognosis via Inducing Monocyte Polarization. Frontiers in Cellular and Infection Microbiology, 15, Article 1531125. [Google Scholar] [CrossRef] [PubMed]
[15] Li, F., Wang, S., Gao, Z., Qing, M., Pan, S., Liu, Y., et al. (2025) Harnessing Artificial Intelligence in Sepsis Care: Advances in Early Detection, Personalized Treatment, and Real-Time Monitoring. Frontiers in Medicine, 11, Article 1510792. [Google Scholar] [CrossRef] [PubMed]
[16] Yao, T., Guan, C., Chen, Q., Wang, P., Xing, N., Liu, Z., et al. (2025) Multi-Omics Nominates VDAC2 as a Candidate Protective Locus in Sepsis-Associated Cholesterol Dysregulation. Apoptosis, 30, 3190-3206. [Google Scholar] [CrossRef] 
[17] Zhang, R., Long, F., Wu, J. and Tan, R. (2025) Distinct Immunological Signatures Define Three Sepsis Recovery Trajectories: A Multi-Cohort Machine Learning Study. Frontiers in Medicine, 12, Article 1575237. [Google Scholar] [CrossRef] [PubMed]
[18] Wang, D., Li, J., Sun, Y., Ding, X., Zhang, X., Liu, S., et al. (2021) A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients. Frontiers in Public Health, 9, Article 7438. [Google Scholar] [CrossRef] [PubMed]
[19] Yang, J.O., Zinter, M.S., Pellegrini, M., Wong, M.Y., Gala, K., Markovic, D., et al. (2023) Whole Blood Transcriptomics Identifies Subclasses of Pediatric Septic Shock. Critical Care, 27, Article No. 486. [Google Scholar] [CrossRef] [PubMed]
[20] Gao, Y., Chen, H., Wu, R. and Zhou, Z. (2025) Ai-Driven Multi-Omics Profiling of Sepsis Immunity in the Digestive System. Frontiers in Immunology, 16, Article 1590526. [Google Scholar] [CrossRef] [PubMed]
[21] Qiu, Y. and Zhou, X. (2025) Data-Driven Spatial-Temporal Framework for Exploring the Heterogeneity and Temporality of Sepsis. Chinese Medical Journal, 139, 34-47. [Google Scholar] [CrossRef] 
[22] Li, J., Zhang, Y., Jiang, F., Shi, X., Tu, M., Liu, Z., et al. (2026) Precision Medicine in Sepsis: Reappraising Glucocorticoid Therapy through the Lens of Molecular Endotypes. Inflammation Research, 75, Article No. 38. [Google Scholar] [CrossRef] 
[23] Zhang, Z., Chen, L., Sun, B., Ruan, Z., Pan, P., Zhang, W., et al. (2024) Identifying Septic Shock Subgroups to Tailor Fluid Strategies through Multi-Omics Integration. Nature Communications, 15, Article No. 9028. [Google Scholar] [CrossRef] [PubMed]
[24] Zhang, X., Zhang, W., Zhang, H. and Liao, X. (2025) Sepsis Subphenotypes: Bridging the Gaps in Sepsis Treatment Strategies. Frontiers in Immunology, 16, Article 1546474. [Google Scholar] [CrossRef] [PubMed]
[25] Zeng, D., Yu, Y., Qiu, W., Ou, Q., Mao, Q., Jiang, L., et al. (2025) Immunotyping the Tumor Microenvironment Reveals Molecular Heterogeneity for Personalized Immunotherapy in Cancer. Advanced Science, 12, e2417593. [Google Scholar] [CrossRef] [PubMed]
[26] Zhang, Z., Chen, L., Shen, H., Wang, J., Yang, J., Yang, S., et al. (2025) Deriving Consensus Sepsis Clusters via Goal-Directed Subgroup Identification in Multi-Omics Study. Nature Communications, 16, Article No. 10328. [Google Scholar] [CrossRef] 
[27] Wang, X., Ning, J., Zhou, L., Li, H., Cui, J., Wang, J., et al. (2025) Targeting Matrix Metalloproteinase-9 to Alleviate T Cell Exhaustion and Improve Sepsis Prognosis. Research, 8, Article ID: 0996. [Google Scholar] [CrossRef] 
[28] Shi, Y., Wu, D., Wang, Y., Shao, Y., Zeng, F., Zhou, D., et al. (2024) Treg and Neutrophil Extracellular Trap Interaction Contributes to the Development of Immunosuppression in Sepsis. JCI Insight, 9, e180132. [Google Scholar] [CrossRef] [PubMed]
[29] Scott, J., Ruchaud-Sparagano, M., Musgrave, K., Roy, A.I., Wright, S.E., Perry, J.D., et al. (2021) Phosphoinositide 3-Kinase δ Inhibition Improves Neutrophil Bacterial Killing in Critically Ill Patients at High Risk of Infection. The Journal of Immunology, 207, 1776-1784. [Google Scholar] [CrossRef] [PubMed]
[30] Mullin, B.H., Ribet, A.B.P. and Pavlos, N.J. (2023) Bone Trans-Omics: Integrating Omics to Unveil Mechanistic Molecular Networks Regulating Bone Biology and Disease. Current Osteoporosis Reports, 21, 493-502. [Google Scholar] [CrossRef] [PubMed]
[31] Yang, L., Teng, S., Ma, Z., Han, C. and Qian, W. (2026) Multi-Omics Machine Learning Identifies Diagnostic Gene Signatures and Functionally Supports PRKACB Involvement in Macrophage Inflammatory Responses in Sepsis. Frontiers in Immunology, 16, Article 1611348. [Google Scholar] [CrossRef] 
[32] Wei, J., Huang, B., Hu, K., Xiong, B. and Xiang, S. (2025) Identification and Validation of Potential Shared Diagnostic Markers for Sepsis-Induced ARDS and Cardiomyopathy via WGCNA and Machine Learning. Frontiers in Molecular Biosciences, 12, Article 1665387. [Google Scholar] [CrossRef] 
[33] Jin, J., Dong, Y., Huang, Y., Wu, L., Yu, L., Sun, Y., et al. (2025) Errα Knockout Promotes M2 Microglial Polarization and Inhibits Ferroptosis in Sepsis-Associated Brain Dysfunction. Molecular Neurobiology, 62, 11834-11847. [Google Scholar] [CrossRef] [PubMed]
[34] Li, X., Ke, G., Hu, Y. and Chen, M. (2026) A Tri-Omics and Machine Learning Framework Identifies Prognostic Biomarkers and Metabolic Signatures in Sepsis. Scientific Reports, 16, Article No. 6648. [Google Scholar] [CrossRef] 
[35] Li, X., Jiang, S., Wang, B., He, S., Guo, X., Lin, J., et al. (2024) Integrated Multi-Omics Analysis and Machine Learning Developed Diagnostic Markers and Prognostic Model Based on Efferocytosis-Associated Signatures for Septic Cardiomyopathy. Clinical Immunology, 265, Article ID: 110301. [Google Scholar] [CrossRef] [PubMed]
[36] Wan, C. and Wang, Y. (2025) Integrated Multi-Omics of Mitophagy-Related Molecular Subtype Characterization and Biomarker Identification in Sepsis. Scientific Reports, 16, Article No. 701. [Google Scholar] [CrossRef] 
[37] Ding, X., Qin, J., Huang, F., Feng, F. and Luo, L. (2023) The Combination of Machine Learning and Untargeted Metabolomics Identifies the Lipid Metabolism-Related Gene CH25H as a Potential Biomarker in Asthma. Inflammation Research, 72, 1099-1119. [Google Scholar] [CrossRef] [PubMed]
[38] Liu, W., Xu, L., Wang, X. and Wang, J. (2026) Integrative Multi-Omics and Network-Based Machine Learning for Early Diagnosis of Parkinson’s Disease. PLOS One, 21, e0329980. [Google Scholar] [CrossRef] 
[39] Huang, Y., Chen, L., Zhang, Z., Liu, Y., Huang, L., Liu, Y., et al. (2025) Integration of Histopathological Image Features and Multi-Dimensional Omics Data in Predicting Molecular Features and Survival in Glioblastoma. Frontiers in Medicine, 12, Article 1510793. [Google Scholar] [CrossRef] [PubMed]
[40] Ni, Y., Hu, B., Wu, G., Shao, Z., Zheng, Y., Zhang, R., et al. (2021) Interruption of Neutrophil Extracellular Traps Formation Dictates Host Defense and Tubular HOXA5 Stability to Augment Efficacy of Anti-Fn14 Therapy against Septic AKI. Theranostics, 11, 9431-9451. [Google Scholar] [CrossRef] [PubMed]
[41] Naito, Y., Goto, D., Hayase, N., Hu, X., Yuen, P.S.T. and Star, R.A. (2026) Peritoneal Neutrophil Extracellular Traps Contribute to Septic AKI via Peritoneal IL-17A and Distant Organ CXCL-1/CXCL-2 Pathway in Abdominal Sepsis. Scientific Reports, 16, Article No. 5446. [Google Scholar] [CrossRef] 
[42] Suo, T., Xu, M. and Fang, J. (2025) Lactylation Modulates Immune Infiltration in Sepsis-Induced Acute Respiratory Distress Syndrome: A Multi-Omics and Machine Learning Study with Experimental Confirmation. European Journal of Medical Research, 30, Article No. 1100. [Google Scholar] [CrossRef] 
[43] Liu, J., Li, X., Yang, P., He, Y., Hong, W., Feng, Y., et al. (2025) LIN28A-Dependent lncRNA NEAT1 Aggravates Sepsis-Induced Acute Respiratory Distress Syndrome through Destabilizing ACE2 mRNA by RNA Methylation. Journal of Translational Medicine, 23, Article No. 15. [Google Scholar] [CrossRef] [PubMed]
[44] shi, Z., Peng, Q., Sun, S., Xiong, M., Zhu, X., Wang, H., et al. (2025) C/EBP β/AEP Pathway Mediates Hippocampal Mitochondrial Damage in a Mouse Model of Sepsis Encephalopathy. International Immunopharmacology, 163, Article ID: 115275. [Google Scholar] [CrossRef] [PubMed]
[45] Zhang, Z., Qiu, X., Zeng, X., Liu, X., Lu, J., Xu, C., et al. (2025) Integrated Multi Omics and Machine Learning Reveal Mitochondrial Immunometabolic Networks in Sepsis Associated Encephalopathy. Scientific Reports, 15, Article No. 33572. [Google Scholar] [CrossRef] 
[46] Wang, Y., Wei, A., Su, Z., Shi, Y., Li, X. and He, L. (2025) Characterization of Lactylation-Based Phenotypes and Molecular Biomarkers in Sepsis-Associated Acute Respiratory Distress Syndrome. Scientific Reports, 15, Article No. 13831. [Google Scholar] [CrossRef] [PubMed]
[47] McClintock, C.R., Mulholland, N. and Krasnodembskaya, A.D. (2022) Biomarkers of Mitochondrial Dysfunction in Acute Respiratory Distress Syndrome: A Systematic Review and Meta-Analysis. Frontiers in Medicine, 9, Article 1011819. [Google Scholar] [CrossRef] [PubMed]
[48] Qian, S., Zheng, R., Shi, Y., Lai, M., Hu, J., Xu, M., et al. (2026) Multi-Omics Integration Reveals Molecular Heterogeneity and Constructs a Machine Learning Survival Model for Sepsis-Induced Coagulopathy. Thrombosis Research, 259, Article ID: 109618. [Google Scholar] [CrossRef] 
[49] Zhuang, J., Huang, H., Jiang, S., Liang, J., Liu, Y. and Yu, X. (2023) A Generalizable and Interpretable Model for Mortality Risk Stratification of Sepsis Patients in Intensive Care Unit. BMC Medical Informatics and Decision Making, 23, Article No. 185. [Google Scholar] [CrossRef] [PubMed]
[50] He, A., Jiang, W., Fu, J., Xu, L., You, C., Li, S., et al. (2025) Dynamic HGI Trajectories and Their Impact on Survival in Patients with Sepsis: A Machine Learning Prognostic Model. Inflammation Research, 74, Article No. 145. [Google Scholar] [CrossRef] 
[51] Chenoweth, J.G., Brandsma, J., Striegel, D.A., Genzor, P., Chiyka, E., Blair, P.W., et al. (2024) Sepsis Endotypes Identified by Host Gene Expression across Global Cohorts. Communications Medicine, 4, Article No. 120. [Google Scholar] [CrossRef] [PubMed]
[52] Li, X., Wu, R., Zhao, W., Shi, R., Zhu, Y., Wang, Z., et al. (2023) Machine Learning Algorithm to Predict Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury. Scientific Reports, 13, Article No. 5223. [Google Scholar] [CrossRef] [PubMed]
[53] Zhou, Y., Feng, J., Mei, S., Zhong, H., Tang, R., Xing, S., et al. (2023) Machine Learning Models for Predicting Acute Kidney Injury in Patients with Sepsis-Associated Acute Respiratory Distress Syndrome. Shock, 59, 352-359. [Google Scholar] [CrossRef] [PubMed]
[54] Fu, J., He, A., Wang, L., Li, X., Yu, J. and Zheng, R. (2025) Interpretable Machine Learning Model for Predicting Delirium in Patients with Sepsis: A Study Based on the MIMIC Data. BMC Infectious Diseases, 25, Article No. 585. [Google Scholar] [CrossRef] [PubMed]
[55] Borisov, N. and Buzdin, A. (2022) Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect. Biomedicines, 10, Article 2318. [Google Scholar] [CrossRef] [PubMed]
[56] Liu, X., Zhang, Z., Tan, C., Ai, Y., Liu, H., Li, Y., et al. (2025) Global Trends in Machine Learning Applications for Single-Cell Transcriptomics Research. Hereditas, 162, Article No. 164. [Google Scholar] [CrossRef] [PubMed]
[57] Mahendran, N. and Vincent P M, D.R. (2023) Deep Belief Network-Based Approach for Detecting Alzheimer’s Disease Using the Multi-Omics Data. Computational and Structural Biotechnology Journal, 21, 1651-1660. [Google Scholar] [CrossRef] [PubMed]
[58] Qiu, W., Qi, B., Lin, W., Zhang, S., Yu, W. and Huang, S. (2022) Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data. Frontiers in Genetics, 13, Article 926927. [Google Scholar] [CrossRef] [PubMed]
[59] Maitre, L., Guimbaud, J., Warembourg, C., Güil-Oumrait, N., Petrone, P.M., Chadeau-Hyam, M., et al. (2022) State-of-the-Art Methods for Exposure-Health Studies: Results from the Exposome Data Challenge Event. Environment International, 168, Article ID: 107422. [Google Scholar] [CrossRef] [PubMed]
[60] Rosier, F., Nuñez, N.F., Torres, M., Loriod, B., Rihet, P. and Pradel, L.C. (2022) Transcriptional Response in a Sepsis Mouse Model Reflects Transcriptional Response in Sepsis Patients. International Journal of Molecular Sciences, 23, Article 821. [Google Scholar] [CrossRef] [PubMed]
[61] Hein, Z.M., Guruparan, D., Okunsai, B., Che Mohd Nassir, C.M.N., Ramli, M.D.C. and Kumar, S. (2025) AI and Machine Learning in Biology: From Genes to Proteins. Biology, 14, Article 1453. [Google Scholar] [CrossRef] 
[62] Jain, S. and Safo, S.E. (2024) DeepIDA-GRU: A Deep Learning Pipeline for Integrative Discriminant Analysis of Cross-Sectional and Longitudinal Multiview Data with Applications to Inflammatory Bowel Disease Classification. Briefings in Bioinformatics, 25, bbae339. [Google Scholar] [CrossRef] [PubMed]
[63] Shao, Y., Cheng, Y., Shah, R.U., Weir, C.R., Bray, B.E. and Zeng-Treitler, Q. (2021) Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcomes. Journal of Medical Systems, 45, Article No. 5. [Google Scholar] [CrossRef] [PubMed]
[64] Gimeno, M., San José-Enériz, E., Villar, S., Agirre, X., Prosper, F., Rubio, A., et al. (2022) Explainable Artificial Intelligence for Precision Medicine in Acute Myeloid Leukemia. Frontiers in Immunology, 13, Article 977358. [Google Scholar] [CrossRef] [PubMed]
[65] Zhang, H., Chen, Y. and Li, F. (2021) Predicting Anticancer Drug Response with Deep Learning Constrained by Signaling Pathways. Frontiers in Bioinformatics, 1, Article 639349. [Google Scholar] [CrossRef] [PubMed]
[66] Menyhárt, O. and Győrffy, B. (2021) Multi-Omics Approaches in Cancer Research with Applications in Tumor Subtyping, Prognosis, and Diagnosis. Computational and Structural Biotechnology Journal, 19, 949-960. [Google Scholar] [CrossRef] [PubMed]
[67] Al-Madhagi, S., Joda, H., Jauset-Rubio, M., Ortiz, M., Katakis, I. and O’Sullivan, C.K. (2018) Isothermal Amplification Using Modified Primers for Rapid Electrochemical Analysis of Coeliac Disease Associated DQB1*02 HLA Allele. Analytical Biochemistry, 556, 16-22. [Google Scholar] [CrossRef] [PubMed]
[68] Sudhakar, P., Alsoud, D., Wellens, J., Verstockt, S., Arnauts, K., Verstockt, B., et al. (2022) Tailoring Multi-Omics to Inflammatory Bowel Diseases: All for One and One for All. Journal of Crohn’s and Colitis, 16, 1306-1320. [Google Scholar] [CrossRef] [PubMed]
[69] Dupras, C. and Bunnik, E.M. (2021) Toward a Framework for Assessing Privacy Risks in Multi-Omic Research and Databases. The American Journal of Bioethics, 21, 46-64. [Google Scholar] [CrossRef] [PubMed]
[70] Kim, D.Y., Lee, J., Choi, J., Shin, H., Lee, J.S. and Kim, E.J. (2026) Spatial Multi-Omics in Precision Medicine: Integrating Biological Insights through Multidisciplinary Collaboration. Seminars in Cancer Biology, 119, 24-37. [Google Scholar] [CrossRef] 
[71] Hou, G.Y., Lal, A., Schulte, P.J., Dong, Y., Kilickaya, O., Gajic, O., et al. (2025) Informing Intensive Care Unit Digital Twins: Dynamic Assessment of Cardiorespiratory Failure Trajectories in Patients with Sepsis. Shock, 63, 573-578. [Google Scholar] [CrossRef] [PubMed]
[72] Yang, H., Guan, L., Xue, Y., Li, X., Gao, L., Zhang, Z., et al. (2025) Longitudinal Multi-Omics Analysis of Convalescent Individuals with Respiratory Sequelae 6-36 Months after COVID-19. BMC Medicine, 23, Article No. 134. [Google Scholar] [CrossRef] [PubMed]
[73] Li, Z., Qu, S., Liang, H., Tang, R., Zhang, X., Lu, F., et al. (2025) Integrative Deep Learning of Spatial Multi-Omics with Switch. Nature Computational Science, 5, 1051-1063. [Google Scholar] [CrossRef] 
[74] Shah, J., Rahman Siddiquee, M.M., Krell-Roesch, J., Syrjanen, J.A., Kremers, W.K., Vassilaki, M., et al. (2023) Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer’s Disease: A Literature Review from a Machine Learning Perspective. Journal of Alzheimer’s Disease, 92, 1131-1146. [Google Scholar] [CrossRef] [PubMed]

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