Hepatocellular carcinoma(HCC),a leading cause of cancer mortality,faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity.Pathomics,an emerging discipline that in...Hepatocellular carcinoma(HCC),a leading cause of cancer mortality,faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity.Pathomics,an emerging discipline that integrates artificial intelligence(AI)with quantitative pathology image analysis,aims to decode disease heterogeneity by extracting high-dimensional features from histopathological specimens.This review highlights how AI-driven pathomics has revolutionized liver cancer management through automated analysis of whole-slide images.Pathomics integrates deep learning with histopathological features to enable precise tumour classification(e.g.,HCC vs cholangiocarcinoma),microvascular invasion(MVI)detection,recurrence risk stratification,and survival prediction.Advanced frameworks such as MVI-AI diagnostic model and CHOWDER demonstrate high accuracy in identifying prognostic biomarkers,whereas multiomics integration links morphometric patterns to molecular signatures(e.g.,EZH2 expression and immune infiltration).Despite these breakthroughs,critical bottlenecks persist,including limited multicentre validation studies,"black box"model interpretability,and clinical workflow integration.Future studies should emphasize AI-enhanced multimodal fusion(radiogenomics and liquid biopsy)and standardized platforms to bridge computational pathology and precision oncology,ultimately improving personalized therapeutic strategies for liver malignancies.This synthesis aims to guide research translation and advance personalized therapeutic strategies for liver malignancies.展开更多
The incidence and prevalence of metabolic dysfunction-associated steatotic liver disease(MASLD)have continued to increase in recent years,making it one of the most common chronic liver diseases worldwide.MASLD is high...The incidence and prevalence of metabolic dysfunction-associated steatotic liver disease(MASLD)have continued to increase in recent years,making it one of the most common chronic liver diseases worldwide.MASLD is highly comorbid with obesity,type 2 diabetes,cardiovascular disease,and chronic kidney disease,posing a serious threat to public health and creating a significant medical and socioeconomic burden.Despite advances in research,current clinical practice still faces considerable challenges in early screening,risk stratification,prognostic prediction,and long-term therapeutic monitoring.Recent advances in artificial intelligence(AI)have provided transformative opportunities to address these challenges.AI has demonstrated unique advantages in imaging interpretation,multiomics integration,electronic health record analysis,and remote health management,significantly improving the accuracy and efficiency of the noninvasive diagnosis,individualized risk stratification,precision therapy,and dynamic disease monitoring of MASLD.In this mini-review,the latest advances in AI applications for MASLD diagnosis and management are systematically summarized,and a forward-looking perspective on the role of AI in enabling the next generation of smart health care systems for MASLD is offered,with the aim of providing theoretical and practical guidance for the clinical management of this disease.展开更多
Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic,transcriptomic,and epigenomic scales.The significant molecular heterogenei...Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic,transcriptomic,and epigenomic scales.The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease.Furthermore,it is increasingly likely that biologic mechanisms underlying the pathobiology of cancer involve multiple molecular entities interacting across functional scales.This has motivated the development of computational approaches that integrate molecular measurements with prior biological knowledge in increasingly intricate ways to enable the discovery of driver genomic aberrations across cancers.Here,we review diverse methodological approaches that have powered significant advances in our understanding of the genomic underpinnings of cancer at the cohort and at the individual tumor scales.We outline the key advances and challenges in the computational discovery of cancer mechanisms while motivating the development of systems biology approaches to comprehensively decode the biologic drivers of this complex disease.展开更多
The advent of single-cell genomic technologies has revolutionized plant cell biology by revealing cellular heterogeneity in plant tissues and organs with unprecedented resolution.Methods such as single-cell transcript...The advent of single-cell genomic technologies has revolutionized plant cell biology by revealing cellular heterogeneity in plant tissues and organs with unprecedented resolution.Methods such as single-cell transcriptomics,epigenomics,and multiomics integration have deepened insights into molecular mechanisms governing plant development,function,and evolutionary adaptation(Xu and Jackson,2023).展开更多
Cancer stem cells(CSCs)constitute a highly plastic and therapy-resistant cell subpopulation within tumors that drives tumor initiation,progression,metastasis,and relapse.Their ability to evade conventional treatments,...Cancer stem cells(CSCs)constitute a highly plastic and therapy-resistant cell subpopulation within tumors that drives tumor initiation,progression,metastasis,and relapse.Their ability to evade conventional treatments,adapt to metabolic stress,and interact with the tumor microenvironment makes them critical targets for innovative therapeutic strategies.Recent advances in single-cell sequencing,spatial transcriptomics,and multiomics integration have significantly improved our understanding of CSC heterogeneity and metabolic adaptability.Metabolic plasticity allows CSCs to switch between glycolysis,oxidative phosphorylation,and alternative fuel sources such as glutamine and fatty acids,enabling them to survive under diverse environmental conditions.Moreover,interactions with stromal cells,immune components,and vascular endothelial cells facilitate metabolic symbiosis,further promoting CSC survival and drug resistance.Despite substantial progress,major hurdles remain,including the lack of universally reliable CSC biomarkers and the challenge of targeting CSCs without affecting normal stem cells.The development of 3D organoid models,CRISPR-based functional screens,and AI-driven multiomics analysis is paving the way for precision-targeted CSC therapies.Emerging strategies such as dual metabolic inhibition,synthetic biology-based interventions,and immune-based approaches hold promise for overcoming CSC-mediated therapy resistance.Moving forward,an integrative approach combining metabolic reprogramming,immunomodulation,and targeted inhibition of CSC vulnerabilities is essential for developing effective CSC-directed therapies.This review discusses the latest advancements in CSC biology,highlights key challenges,and explores future perspectives on translating these findings into clinical applications.展开更多
Rice biology research involves complex decision-making,requiring researchers to navigate a rapidly expanding body of knowledge encompassing extensive literature and multiomics data.The exponential increase in biologic...Rice biology research involves complex decision-making,requiring researchers to navigate a rapidly expanding body of knowledge encompassing extensive literature and multiomics data.The exponential increase in biological data and scientific publications presents significant challenges for efficiently extracting meaningful insights.Although large language models(LLMs)show promise for knowledge retrieval,their application to rice-specific research has been limited by the absence of specialized models and the challenge of synthesizing multimodal data integral to the field.Moreover,the lack of standardized evaluation frameworks for domain-specific tasks impedes the effective assessment of model performance.To address these challenges,we introduce SeedLLM·Rice(SeedLLM),a 7-billion-parameter model trained on 1.4 million rice-related publications,representing nearly 98.24%of global rice research output.Additionally,we present a novel human-centric evaluation framework designed to assess LLM performance in rice biology tasks.Initial evaluations demonstrate that SeedLLM outperforms general-purpose models,including OpenAI GPT-4o1 and DeepSeek-R1,achieving win rates of 57%to 88%on rice-specific tasks.Furthermore,SeedLLM is integrated with the Rice Biological Knowledge Graph(RBKG),which consolidates genome annotations for Nipponbare and large-scale synthesis of transcriptomic and proteomic information from over 1800 studies.This integration enhances the ability of SeedLLM to address complex research questions requiring the fusion of textual and multiomics data.To facilitate global collaboration,we provide free access to SeedLLM and the RBKG via an interactive web portal(https://seedllm.org.cn/).SeedLLM represents a transformative tool for rice biology research,enabling unprecedented discoveries in crop improvement and climate adaptation through advanced reasoning and comprehensive data integration.展开更多
基金Supported by Wenzhou Municipal Science and Technology Bureau,No.Y20240109.
文摘Hepatocellular carcinoma(HCC),a leading cause of cancer mortality,faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity.Pathomics,an emerging discipline that integrates artificial intelligence(AI)with quantitative pathology image analysis,aims to decode disease heterogeneity by extracting high-dimensional features from histopathological specimens.This review highlights how AI-driven pathomics has revolutionized liver cancer management through automated analysis of whole-slide images.Pathomics integrates deep learning with histopathological features to enable precise tumour classification(e.g.,HCC vs cholangiocarcinoma),microvascular invasion(MVI)detection,recurrence risk stratification,and survival prediction.Advanced frameworks such as MVI-AI diagnostic model and CHOWDER demonstrate high accuracy in identifying prognostic biomarkers,whereas multiomics integration links morphometric patterns to molecular signatures(e.g.,EZH2 expression and immune infiltration).Despite these breakthroughs,critical bottlenecks persist,including limited multicentre validation studies,"black box"model interpretability,and clinical workflow integration.Future studies should emphasize AI-enhanced multimodal fusion(radiogenomics and liquid biopsy)and standardized platforms to bridge computational pathology and precision oncology,ultimately improving personalized therapeutic strategies for liver malignancies.This synthesis aims to guide research translation and advance personalized therapeutic strategies for liver malignancies.
基金Supported by Shanghai Pujiang Program,No.24PJD071National Natural Science Foundation of China,No.82100605Star Program of Shanghai Jiao Tong University,No.YG2021QN54.
文摘The incidence and prevalence of metabolic dysfunction-associated steatotic liver disease(MASLD)have continued to increase in recent years,making it one of the most common chronic liver diseases worldwide.MASLD is highly comorbid with obesity,type 2 diabetes,cardiovascular disease,and chronic kidney disease,posing a serious threat to public health and creating a significant medical and socioeconomic burden.Despite advances in research,current clinical practice still faces considerable challenges in early screening,risk stratification,prognostic prediction,and long-term therapeutic monitoring.Recent advances in artificial intelligence(AI)have provided transformative opportunities to address these challenges.AI has demonstrated unique advantages in imaging interpretation,multiomics integration,electronic health record analysis,and remote health management,significantly improving the accuracy and efficiency of the noninvasive diagnosis,individualized risk stratification,precision therapy,and dynamic disease monitoring of MASLD.In this mini-review,the latest advances in AI applications for MASLD diagnosis and management are systematically summarized,and a forward-looking perspective on the role of AI in enabling the next generation of smart health care systems for MASLD is offered,with the aim of providing theoretical and practical guidance for the clinical management of this disease.
基金supported by PHS awards:T32 CA094186 to S.K.,K25 DK115904 to V.V.,P30 CA043703 to V.V.,and P20 CA233216 to V.V.
文摘Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic,transcriptomic,and epigenomic scales.The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease.Furthermore,it is increasingly likely that biologic mechanisms underlying the pathobiology of cancer involve multiple molecular entities interacting across functional scales.This has motivated the development of computational approaches that integrate molecular measurements with prior biological knowledge in increasingly intricate ways to enable the discovery of driver genomic aberrations across cancers.Here,we review diverse methodological approaches that have powered significant advances in our understanding of the genomic underpinnings of cancer at the cohort and at the individual tumor scales.We outline the key advances and challenges in the computational discovery of cancer mechanisms while motivating the development of systems biology approaches to comprehensively decode the biologic drivers of this complex disease.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04076)the China Postdoctoral Science Foundation(2023M743078)by NSF grant 2129189 to D.J.
文摘The advent of single-cell genomic technologies has revolutionized plant cell biology by revealing cellular heterogeneity in plant tissues and organs with unprecedented resolution.Methods such as single-cell transcriptomics,epigenomics,and multiomics integration have deepened insights into molecular mechanisms governing plant development,function,and evolutionary adaptation(Xu and Jackson,2023).
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00207904)supported by the Global-Learning&Academic Research Institution for Master’s·PhD students and the Postdocs(LAMP)Program of the National Research Foundation of Korea(NRF)grant funded by the Ministry of Education(No.RS-2023—00301938).
文摘Cancer stem cells(CSCs)constitute a highly plastic and therapy-resistant cell subpopulation within tumors that drives tumor initiation,progression,metastasis,and relapse.Their ability to evade conventional treatments,adapt to metabolic stress,and interact with the tumor microenvironment makes them critical targets for innovative therapeutic strategies.Recent advances in single-cell sequencing,spatial transcriptomics,and multiomics integration have significantly improved our understanding of CSC heterogeneity and metabolic adaptability.Metabolic plasticity allows CSCs to switch between glycolysis,oxidative phosphorylation,and alternative fuel sources such as glutamine and fatty acids,enabling them to survive under diverse environmental conditions.Moreover,interactions with stromal cells,immune components,and vascular endothelial cells facilitate metabolic symbiosis,further promoting CSC survival and drug resistance.Despite substantial progress,major hurdles remain,including the lack of universally reliable CSC biomarkers and the challenge of targeting CSCs without affecting normal stem cells.The development of 3D organoid models,CRISPR-based functional screens,and AI-driven multiomics analysis is paving the way for precision-targeted CSC therapies.Emerging strategies such as dual metabolic inhibition,synthetic biology-based interventions,and immune-based approaches hold promise for overcoming CSC-mediated therapy resistance.Moving forward,an integrative approach combining metabolic reprogramming,immunomodulation,and targeted inhibition of CSC vulnerabilities is essential for developing effective CSC-directed therapies.This review discusses the latest advancements in CSC biology,highlights key challenges,and explores future perspectives on translating these findings into clinical applications.
基金SeedLLM research and development were supported by the Yazhouwan National Laboratory Project(grant no.2310CF01)the Shanghai Artificial Intelligence Laboratory+1 种基金Corpus preparation was supported by the Hainan Yazhou Bay Seed Laboratory Project(grant no.B21HJ0001)Human evaluation of LLM performance was supported by the Biological Breeding-National Science and Technology Major Project(grant no.2023ZD04076).
文摘Rice biology research involves complex decision-making,requiring researchers to navigate a rapidly expanding body of knowledge encompassing extensive literature and multiomics data.The exponential increase in biological data and scientific publications presents significant challenges for efficiently extracting meaningful insights.Although large language models(LLMs)show promise for knowledge retrieval,their application to rice-specific research has been limited by the absence of specialized models and the challenge of synthesizing multimodal data integral to the field.Moreover,the lack of standardized evaluation frameworks for domain-specific tasks impedes the effective assessment of model performance.To address these challenges,we introduce SeedLLM·Rice(SeedLLM),a 7-billion-parameter model trained on 1.4 million rice-related publications,representing nearly 98.24%of global rice research output.Additionally,we present a novel human-centric evaluation framework designed to assess LLM performance in rice biology tasks.Initial evaluations demonstrate that SeedLLM outperforms general-purpose models,including OpenAI GPT-4o1 and DeepSeek-R1,achieving win rates of 57%to 88%on rice-specific tasks.Furthermore,SeedLLM is integrated with the Rice Biological Knowledge Graph(RBKG),which consolidates genome annotations for Nipponbare and large-scale synthesis of transcriptomic and proteomic information from over 1800 studies.This integration enhances the ability of SeedLLM to address complex research questions requiring the fusion of textual and multiomics data.To facilitate global collaboration,we provide free access to SeedLLM and the RBKG via an interactive web portal(https://seedllm.org.cn/).SeedLLM represents a transformative tool for rice biology research,enabling unprecedented discoveries in crop improvement and climate adaptation through advanced reasoning and comprehensive data integration.
