Based On The Integration Of Multi-omics Data And The TCM Syndrome Differentiation And Classification,for The Purpose To Analysis Of The Mechanism Of Human Femoral Head Collapse In Femoral Head Ischemia Necrosis And The Construction Of A Predictive Diagnos

Objective:This study aimed to investigate the molecular mechanisms related to the collapse of osteonecrosis of the femoral head(ONFH)by integrating multi-omics data,including genomics,transcriptomics,and proteomics,within the framework of Traditional Chinese Medicine(TCM)syndrome differentiation.The goal is to identify relevant biomarkers and key pathways and to employ machine learning and deep learning techniques to construct high-precision predictive diagnostic models for ONFH collapse,thereby enhancing the accuracy of clinical diagnoses and prognostic predictions.Methods:By conducting a comprehensive analysis of existing omics databases and literature,this study gathered extensive ONFH-related omics data.Preliminary bioinformatics analysis uncovered the potential molecular mechanisms of ONFH,which were further elucidated through integrated multi-omics analysis.Coupled with TCM syndrome differentiation,a variety of machine learning and deep learning algorithms have been applied to model and predict ONFH collapse with an assessment of the predictive efficacy of different models.Results:The omics analyses in this study revealed several biological processes and pathways strongly associated with ONFH collapse,including neuronal synapse development on the cell membrane,intercellular adhesion,potassium ion transport,GTPase-mediated signal regulation,and mitochondrial function.16S metagenomic analysis highlighted the potential role of gut microbiota in ONFH collapse.Proteomic and metabolomic analyses have enriched our understanding of the mechanisms underlying ONFH.The integration of multi-omics data has uncovered key signaling pathways and biological processes,providing new biomarkers for predicting ONFH collapse.The application of machine learning and deep learning models demonstrated their potential for high-precision diagnosis and prediction of ONFH collapses.Conclusion(s):This study underscores the critical importance of integrative multi-omics analysis in comprehending the complex mechanisms of ONFH,and demonstrates the significant potential of machine learning and deep learning technologies in enhancing the predictive accuracy of ONFH collapse diagnoses.The integration of TCM syndrome differentiation offers a novel perspective for the personalized treatment of ONFH.These findings have significant implications for the development of new treatment strategies and personalized medicine.