The rapid development of biological and medical examination methods has vastly expanded personal biomedical information,including molecular,cel-lular,image,and electronic health record datasets.Integrating this wealth...The rapid development of biological and medical examination methods has vastly expanded personal biomedical information,including molecular,cel-lular,image,and electronic health record datasets.Integrating this wealth of information enables precise disease diagnosis,biomarker identification,and treatment design in clinical settings.Artificial intelligence(Al)techniques,particularly deep learning models,have been extensively employed in biomedical applications,demonstrating increased precision,efficiency,and generalization.The success of the large language and vision models fur-ther significantly extends their biomedical applications.However,challenges remain in learning these multimodal biomedical datasets,such as data privacy,fusion,and model interpretation.In this review,we provide a comprehensive overview of various biomedical data modalities,multimodal rep-resentation learning methods,and the applications of Al in biomedical data integrative analysis.Additionally,we discuss the challenges in applying these deep learning methods and how to better integrate them into biomedical scenarios.We then propose future directions for adapting deep learn-ing methods with model pretraining and knowledge integration to advance biomedical research and benefit their clinical applications.展开更多
Advances in multi-omics datasets and analytical methods have revolutionized cancer research,offering a comprehensive,pan-cancer perspective.Pancancer studies identify shared mechanisms and unique traits across differe...Advances in multi-omics datasets and analytical methods have revolutionized cancer research,offering a comprehensive,pan-cancer perspective.Pancancer studies identify shared mechanisms and unique traits across different cancer types,which are reshaping diagnostic and treatment strategies.However,continued innovation is required to refine these approaches and deepen our understanding of cancer biology and medicine.This review summarized key findings from pan-cancer research and explored their potential to drive future advancements in oncology。展开更多
The coronavirus disease 2019(COVID-19)pandemic had a devastating impact on human society.Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the development of omics techn...The coronavirus disease 2019(COVID-19)pandemic had a devastating impact on human society.Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19.Here,we reviewed how omics,including genomics,proteomics,single-cell multi-omics,and clinical phenomics,play roles in answering biological and clinical questions about COVID-19.Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification.Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement.Furthermore,decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.展开更多
The development of the term‘imaging genomics’.Despite significant advancements in human genomics,the phenotypic and clinical relevance of genomic features remains largely unknown.Imaging genomics,also known as radio...The development of the term‘imaging genomics’.Despite significant advancements in human genomics,the phenotypic and clinical relevance of genomic features remains largely unknown.Imaging genomics,also known as radiogenomics,was proposed to find the associations between clinical image data and human genetic data,facilitating a better understanding of the molecular characteristics of diseases.Originally,imaging data are acquired mainly by Magnetic Resonance Imaging(MRI)for brain disease detection.展开更多
基金supported by the National Key R&D Program(Grant Nos.2023YFF1204701 and 2022YFF1202101)the Self-supporting Program of Guangzhou Laboratory(Grant No.SRPG22007)+1 种基金the CAS Research Fund(Grant No.XDB38050200)Guangdong Basic and Applied Basic Research Foundation(Grant No.2023B1515130008),China.
文摘The rapid development of biological and medical examination methods has vastly expanded personal biomedical information,including molecular,cel-lular,image,and electronic health record datasets.Integrating this wealth of information enables precise disease diagnosis,biomarker identification,and treatment design in clinical settings.Artificial intelligence(Al)techniques,particularly deep learning models,have been extensively employed in biomedical applications,demonstrating increased precision,efficiency,and generalization.The success of the large language and vision models fur-ther significantly extends their biomedical applications.However,challenges remain in learning these multimodal biomedical datasets,such as data privacy,fusion,and model interpretation.In this review,we provide a comprehensive overview of various biomedical data modalities,multimodal rep-resentation learning methods,and the applications of Al in biomedical data integrative analysis.Additionally,we discuss the challenges in applying these deep learning methods and how to better integrate them into biomedical scenarios.We then propose future directions for adapting deep learn-ing methods with model pretraining and knowledge integration to advance biomedical research and benefit their clinical applications.
基金supported by the Self-supporting Program of Guangzhou Laboratory(SRPG22007)the CAS Research Fund(XDB38050200)+1 种基金the National Key Research and Development Program of China(2021YFA0910100)Healthy Zhejiang One Million People Cohort(K-20230085).
文摘Advances in multi-omics datasets and analytical methods have revolutionized cancer research,offering a comprehensive,pan-cancer perspective.Pancancer studies identify shared mechanisms and unique traits across different cancer types,which are reshaping diagnostic and treatment strategies.However,continued innovation is required to refine these approaches and deepen our understanding of cancer biology and medicine.This review summarized key findings from pan-cancer research and explored their potential to drive future advancements in oncology。
基金We thank Professor S.Y.Liu for her revision suggestions for the article’s first draft.We acknowledge support from the CAS Research Fund,Grant No.XDB38050200the Self-supporting Program of Guangzhou Laboratory,Grant No.SRPG22-001 and SRPG22-007.
文摘The coronavirus disease 2019(COVID-19)pandemic had a devastating impact on human society.Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19.Here,we reviewed how omics,including genomics,proteomics,single-cell multi-omics,and clinical phenomics,play roles in answering biological and clinical questions about COVID-19.Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification.Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement.Furthermore,decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.
基金supported by the Self-supporting Program of Guangzhou Laboratory(SRPG22007)the CAS Research Fund(XDB38050200).
文摘The development of the term‘imaging genomics’.Despite significant advancements in human genomics,the phenotypic and clinical relevance of genomic features remains largely unknown.Imaging genomics,also known as radiogenomics,was proposed to find the associations between clinical image data and human genetic data,facilitating a better understanding of the molecular characteristics of diseases.Originally,imaging data are acquired mainly by Magnetic Resonance Imaging(MRI)for brain disease detection.
