Advancements in SinoBioData Intelligence: A Comprehensive Review of Integrative Omics and Machine Learning in Precision Medicine
Authors: CHEN Xia, WANG Yu, LI Jing, ZHANG Wei
The rapid evolution of high-throughput technologies has generated an unprecedented volume of biomedical data, necessitating sophisticated integrative approaches to translate this wealth into actionable clinical insights. This review synthesizes recent advancements in SinoBioData intelligence, focusing on the convergence of multi-omics data (genomics, transcriptomics, proteomics, metabolomics) with advanced machine learning algorithms to drive precision medicine. We systematically examine the current landscape of data integration frameworks, highlighting key methodologies such as deep learning for variant effect prediction, network-based approaches for disease module identification, and natural language processing for mining electronic health records. Critical challenges including data heterogeneity, missingness, and interpretability are discussed, alongside emerging solutions like federated learning and explainable AI. Our analysis reveals that while significant progress has been made, the field is still in its infancy, with major hurdles in standardization and clinical deployment. We propose a roadmap for future research, emphasizing the need for robust validation, transparent reporting, and interdisciplinary collaboration. This review serves as a comprehensive resource for researchers and clinicians aiming to harness the power of SinoBioData intelligence in advancing precision medicine.
