其昊生物
BIOMEDICAL RESEARCH INSIGHTS

洞察前沿，连接科研新知

聚焦合成生物学、AI 靶点发现、临床队列与公共数据库，汇集最新论文摘要与原始出处
每 12 小时采集更新，经人工审核后发布。

前沿研究专业聚合持续更新
PAPER EXPLORER

发现热点论文

每 5 分钟更新缓存
全部共 21 篇
临床队列临床研究Immunity, inflammation and disease

Growth Hormone-Related Gene Signatures Reveal Diagnostic Biomarkers, Immune Landscapes, and Potential Therapeutic Targets in Sepsis Through WGCNA and Machine Learning Analyses.

Xiuhua Li、Ang Liu、Yingwei Ding 等DOI: 10.1002/iid3.70566
GEO机器学习风险模型队列研究
暂无引用数据

脓毒症会重塑炎症、免疫和代谢活动，但生长激素相关基因在该反应中的表现尚不明确。研究整合两个GEO芯片队列构建模型，并以GSE26378和GSE28750独立验证；通过差异分析与WGCNA识别脓毒症相关表达信号，再与1205个生长激素相关基因取交集，经蛋白互作和四种机器学习方法筛选候选基因。结果获得2552个差异表达基因和1873个WGCNA模块基因，交集含40个基因，PPI分析缩减至23个枢纽候选，机器学习一致保留JAK1和GATA3；二者在脓毒症中均下调，在训练及外部验证队列中诊断性能稳定，联合模型判别能力优于单基因。功能分析显示两基因与T细胞信号、抗原加工、免疫调节、线粒体代谢及翻译过程相关；免疫图谱显示脓毒症中淋巴细胞相关特征减弱、髓系特征增强；按JAK1/GATA3表达可分两个免疫和转录谱不同的亚型。研究表明JAK1和GATA3是有前景的生长激素相关候选诊断生物标志物，与脓毒症免疫异质性相关，其联合特征或可用于分子分层，尚需进一步实验和临床验证。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Xiuhua Li、Ang Liu、Yingwei Ding、Gang Chen、Bingwen Zhang

作者机构

  • Department of Emergency Medicine,, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, China.

技术路线

暂未整理

最近 5 个完整年度引用趋势

暂无引用数据

数据来源：暂未标注更新时间：暂无更新时间
探索相关选题直接查重
查看原文
AI 靶点发现临床研究International journal of molecular sciences

A Mechanistic Map of the Phenotypic Complexity in Recessive Dystrophic Epidermolysis Bullosa Provides Basis for Therapeutic Target Discovery and Drug Repurposing.

Sara Herráiz-Gil、Miriam Payá-Milans、María Del Carmen de Arriba 等DOI: 10.3390/ijms27198436
肿瘤与癌症机器学习儿童
0 次引用趋势数据不足

隐性营养不良型大疱性表皮松解症（RDEB）由COL7A1基因功能缺失突变所致，表型复杂异质，临床可见皮肤脆性增加、局部及全身炎症、伤口愈合障碍、菌群失调、疼痛和瘙痒，并增加皮肤癌风险。本研究旨在构建RDEB分子机制的可操作模型，以识别并优选药物重定位的治疗靶点。研究利用儿童RDEB患者与未受累个体皮肤活检的转录组数据构建疾病机制图谱，并结合机器学习从图谱回路中推断可操作靶点、识别可逆转转录组改变的候选药物。所得图谱包含13条改变信号回路和10个效应蛋白，与炎症、纤维化及肿瘤进展等疾病特征相关；分析识别出66个可操作靶点及74个候选药物，富集分析显示磷酸二酯酶类及抗肿瘤和免疫调节剂富集。最终优先选出16种可逆转回路改变且不增强非预期效应的药物，其中苄氟噻嗪和伊布利特影响最大。该机制模型提供了首个RDEB功能性疾病图谱，并揭示了由已批准药物靶向的有前景的治疗候选物。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Sara Herráiz-Gil、Miriam Payá-Milans、María Del Carmen de Arriba、Lucía Martínez-Santamaría、Carlos Loucera、Nuria Illera、Marcela Del Río、Joaquín Dopazo、Carlos León、María José Escámez

作者机构

  • Departamento de Bioingeniería, Universidad Carlos III de Madrid, ROR Code 03ths8210, Avenida de la Universidad, 30 (Edificio Sabatini), 28911 Leganés, Spain.
  • Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), 28029 Madrid, Spain.
  • Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD, UAM), 28040 Madrid, Spain.
  • Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, 28040 Madrid, Spain.
  • Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, 41092 Seville, Spain.
  • Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, 41013 Seville, Spain.

技术路线

暂未整理

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 03:55
探索相关选题直接查重
查看原文
临床队列综述Nature medicine

On-premise medical AI agents for reliable clinical decision-making.

Li Zhang、Georg Wölflein、Dyke Ferber 等DOI: 10.1038/s41591-026-04609-x
MIMIC机器学习风险模型成人方法学
2 次引用趋势数据不足

研究开发并评估了一种完全本地部署的临床人工智能代理，将本地运行控制与多视角可靠性框架结合，以支持选择性自主决策。该代理在两项基于MIMIC-IV的基准测试中，分别于七疾病任务和四疾病任务上达到90.04%和83.8%的准确率，主要基准接近云端基线。为评估决策时可靠性，研究量化了诊断与推理的内部似然、语言及行为稳定性指标。诊断行为一致性对正确性的判别力最强（AUC=0.860），在压力测试下仍具信息性（AUC=0.875）。当一致性阈值为0.90时，49.4%的病例被保留，诊断准确率达98.9%。结论表明，决策时可靠性信号可识别低风险子集供自主处理，其余转交人工审核，为机构治理下的临床代理提供了实用框架。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Li Zhang、Georg Wölflein、Dyke Ferber、Junhao Liang、Zunamys I Carrero、Xuewei Wu、Julien Vibert、Jan Clusmann、Lino Möhrmann、Elena E Möhrmann、Catharina Wichmann、Fabian Wolf、Tim Lenz、Jakob Nikolas Kather

作者机构

  • Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.
  • Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
  • Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Drug Development Department, Gustave Roussy, Villejuif, France.
  • Université Paris-Saclay, Gustave Roussy, INSERM U981, IHU PRISM, Villejuif, France.
  • Department of Medicine I, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.
  • Department of Gynecology and Obstetrics, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.
  • Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany. kather.jn@tu-dresden.de.
  • Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. kather.jn@tu-dresden.de.
  • Department of Medicine I, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany. kather.jn@tu-dresden.de.
  • Pathology & Data Analytics, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK. kather.jn@tu-dresden.de.

技术路线

数据/样本：两个基于MIMIC-IV的基准（七疾病任务和四疾病任务） → 分析：开发并评估一个完全本地部署的临床智能体，耦合本地操作控制与多视角可靠性框架；量化诊断和推理的内部似然、语言和行为稳定性指标；评估诊断正确性的判别能力（AUC）及压力测试下的信息性；设定一致性阈值评估保留病例比例及诊断准确率

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:04
探索相关选题直接查重
查看原文
公共数据库临床研究Science translational medicine

Machine learning-driven spleen imaging and genomics uncover a splenic connection to coronary artery disease.

Meghana Kamineni、Vineet Raghu、Zhanqing Hua 等DOI: 10.1126/scitranslmed.aeh2517
UK Biobank心血管疾病机器学习深度学习方法学
0 次引用趋势数据不足

利用深度学习从英国生物银行42059名及Mass General Brigham生物银行2745名参与者的腹部MRI中提取107个脾脏影像组学特征，其中10个特征与冠状动脉疾病相关。对这些特征的基因组关联分析识别出219个位点，包括9p21；该位点变异与脾脏连续纹理区域异质性等特征相关。研究MRI结果内部一致，但外部临床验证因成像方案差异和患者临床异质性面临转化挑战。研究结合深度学习与基因组学，揭示脾脏可能参与冠状动脉疾病，并强调研究与临床影像组学间的转化差距。

为什么值得关注

编辑短评

该研究整合腹部MRI深度学习影像组学与基因组学，在大型队列中提取脾脏特征并关联CAD，进而识别219个基因座（含9p21），为脾脏参与CAD提供探索性框架。但关联分析不能确立因果，影像组学特征生物学可解释性有限，外部临床验证暴露协议与人群异质性导致的转化缺口，结论宜定位为假说生成。

查看扩展信息

完整作者

Meghana Kamineni、Vineet Raghu、Zhanqing Hua、Haodong Tian、Buu Truong、Ahmed Alaa、Art Schuermans、Sam Friedman、Christopher Reeder、Romit Bhattacharya、Peter Libby、Patrick T Ellinor、Mahnaz Maddah、Anthony Philippakis、Whitney Hornsby、Zhi Yu、Pradeep Natarajan

作者机构

  • Harvard Medical School, Boston, MA 02115, USA.
  • Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
  • Artificial Intelligence in Medicine Program, Mass General Brigham, Boston, MA 02114, USA.
  • Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
  • Heart and Vascular Institute, Mass General Brigham, Boston, MA 02114, USA.
  • Computational Precision Health Program, University of California, Berkeley, Berkeley, CA 94720, USA.
  • Computational Precision Health Program, University of California, San Francisco, San Francisco, CA 94143, USA.
  • Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium.
  • Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Layer Health, Boston, MA 02111, USA.
  • Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.
  • Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
  • Google Ventures, Cambridge, MA 02142, USA.
  • Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
  • Personalized Medicine, Mass General Brigham, Boston, MA 02114, USA.
  • Amgen Inc., Thousand Oaks, CA 91320, USA.

技术路线

数据/样本：UK Biobank 42,059例与MGBB 2,745例腹部MRI；分析：深度学习提取107个脾脏影像组学特征→与CAD关联分析→对CAD相关特征行全基因组关联分析；验证：研究MRI内部一致，外部临床验证发现影像协议差异和临床异质性带来转化挑战

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:05
探索相关选题直接查重
查看原文
临床队列综述Briefings in bioinformatics

Advances in computational biology approaches for digital modeling of tumor microenvironment.

作者信息暂未整理DOI: 10.1093/bib/bbag595
肿瘤与癌症机器学习综述
暂无引用数据

肿瘤微环境（TME）是异质性多尺度系统，仅靠实验难以完整刻画。计算建模可整合细胞、血管、代谢、免疫与力学机制，但方法多样，给模型选择、参数化、验证和临床转化带来困难。本文以问题为导向综述TME建模的计算方法，比较连续、离散及离散-连续混合范式在生物学分辨率、数据需求、可解释性和计算成本上的差异，并将其关联至肿瘤细胞动力学、血管运输、细胞外基质与力学、代谢与缺氧、免疫互作五个机制领域；从参数可识别性、不确定性量化、数值有效性、生物学保真度和临床相关性考察模型可信度，强调按预期用途和验证情境解读性能。文章还分析机器学习在机制-ML整合中的功能，包括代理模型加速前向模拟、反演方法估计潜参数与状态、患者特异性工作流整合多模态临床数据与机制模型，以及物理信息神经网络嵌入控制方程；并讨论纵向个性化、物理感知多尺度整合、临床可部署数字孪生、外部泛化和合成数据到临床迁移，提出从机制假设生成到自适应数字孪生决策支持的转化就绪路径。可信的TME建模应优先采用最小充分模型，明确预期用途，透明呈现不确定性，并使证据与生物学或临床问题相匹配。

为什么值得关注

暂未整理

查看扩展信息

完整作者

暂未整理

作者机构

暂未整理

技术路线

暂未整理

最近 5 个完整年度引用趋势

暂无引用数据

数据来源：暂未标注更新时间：暂无更新时间
探索相关选题直接查重
查看原文
AI 靶点发现综述Nature

Towards the construction of a virtual yeast.

Liujia Qian、Zizhuo Zhou、Peijie Zhou 等DOI: 10.1038/s41586-026-10574-9
机器学习方法学
5 次引用趋势数据不足

为推进细胞生命的计算模拟，研究者提出“虚拟酵母”——一种由人工智能驱动的代理，以酿酒酵母为遗传可操作、数据丰富的模式系统，通过整合多模态生物学数据、机制推理与主动实验来模拟真核细胞行为。该系统将细胞复杂性分解为遗传、代谢和结构系统等八个以功能为中心的模块，每个模块由领域专用AI工具实现，并通过基于大语言模型的编排层协调。系统以机制知识、亚细胞结构和动态状态三大数据支柱为基础，在闭环学习流程中整合表征学习与生成建模，自主设计和执行实验。虚拟酵母可作为优化生物合成通路、支持多种细胞过程假设生成与优先级排序以及加速靶点发现的概念与操作平台，为构建虚拟真核细胞和推进合成生物学提供可推广的蓝图。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Liujia Qian、Zizhuo Zhou、Peijie Zhou、Zhen Dong、Xudong Zhang、Zhenwu Dai、Zhangyang Gao、Siqi Sun、Kevin R Roy、Shuaiyao Wang、Nicola Zamboni、Charles Boone、Michael Costanzo、Jianzhe Li、Gianni Liti、Jia-Xing Yue、Markus Ralser、Evan Williams、Mattia Zampieri、Heng Jiang、Tailin Wu、Yalin Wang、Feiran Li、Joseph Schacherer、Rui Sun、Zhaoxing Li、Yaming Deng、Yi Chen、Zhiping Xie、Huiqiang Lou、Xiaowen Wang、Linhai Xie、Han Wen、Liangyi Chen、Kai Lei、George Rosenberger、Xue Cai、Yingrui Wang、Qi Xiao、Huaizong Shen、Gaowen Liu、Lei Ma、Brenda Andrews、Hui Lu、Kiryl Piatkevich、Yi Zhu、Lei Bai、Yizhi Cai、Yuping Chen、Weinan E、Ge Gao、Fuchu He、Luonan Chen、Stan Z Li、Hongwu Ma、Liang Qiao、Lars M Steinmetz、Leihan Tang、Tang Tang、Xiaofan Zhang、Jing Yang、Yifan Yang、Kaicheng Yu、Jianyang Zeng、Yefeng Zheng、Bowen Zhou、Tiannan Guo

作者机构

  • Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, School of Future Biomedicine, Westlake University, Hangzhou, China.
  • Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, China.
  • Center for Machine Learning Research, Peking University, Beijing, China.
  • AI for Science Institute, Beijing, China.
  • National Engineering Laboratory for Big Data Analysis and Applications, Beijing, China.
  • Center for Data Science, Peking University, Beijing, China.
  • Shanghai Artificial Intelligence Laboratory, Shanghai, China.
  • Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
  • Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.
  • Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
  • The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
  • Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
  • Institute of Research on Cancer and Ageing of Nice (IRCAN), Faculté de Médecine, Nice, France.
  • State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Department of Biochemistry, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité, Berlin, Germany.
  • Center for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Max Planck Institute for Molecular Genetics, Berlin, Germany.
  • Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
  • Department of Biomedicine, University of Basel, Basel, Switzerland.
  • Department of Artificial Intelligence, School of Engineering, Westlake University, Hangzhou, China.
  • Biomedical Research Core Facilities, Westlake University, Hangzhou, China.
  • Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
  • Université de Strasbourg, CNRS, Inserm, IGBMC UMR 7104-UMR-S 1258, Illkirch, France.
  • State Key Laboratory of Microbial Metabolism and Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • South China Hospital, Health Science Center, Guangdong Key Laboratory of Genome Instability and Disease Prevention, Shenzhen University School of Medicine, Shenzhen, China.
  • State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics Driven Cancer Precision Medicine (Chinese Academy of Medical Sciences), Beijing, China.
  • International Academy of Phronesis Medicine (Guangdong), Guangzhou, China.
  • DP Technology Co. Ltd., Beijing, China.
  • Beijing Advanced Center of RNA Biology (BEACON), Peking University, Beijing, China.
  • State Key Laboratory of Medical Proteomics, Beijing, China.
  • State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.
  • Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
  • Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China.
  • Bruker Switzerland AG, Faellanden, Switzerland.
  • Zhejiang Key Laboratory of Structural Biology, School of Life Sciences, Westlake University, Hangzhou, China.
  • Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • National Biomedical Imaging Center, College of Future Technology, Peking University, Beijing, China.
  • State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, Shanghai, China.
  • Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, China.
  • School of Life Sciences, Westlake University, Hangzhou, China.
  • Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
  • Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
  • Generative and Synthetic Genomics, Wellcome Sanger Institute, Cambridge, UK.
  • Shenzhen Institute of Synthetic Biology, Chinese Academy of Sciences, Shenzhen, China.
  • Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • School of Mathematical Sciences, Peking University, Beijing, China.
  • State Key Laboratory of Gene Function and Modulation Research, Biomedical Pioneering Innovative Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Center for Bioinformatics (CBI), Peking University, Beijing, China.
  • School of Mathematical Sciences and School of AI, Shanghai Jiao Tong University, Shanghai, China.
  • AI Laboratory, Research Center for Industries of the Future, Westlake University, Hangzhou, China.
  • Biodesign Center, State Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
  • National Center of Technology Innovation for Synthetic Biology, Tianjin, China.
  • Department of Chemistry, Fudan University, Shanghai, China.
  • Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
  • DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany.
  • Stanford Genome Technology Center, Palo Alto, CA, USA.
  • Center for Interdisciplinary Studies, Westlake University, Hangzhou, China.
  • Wuhan Metware Biotechnology Co. Ltd., Wuhan, China.
  • Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China.
  • State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing Institute of Lifeomics, Beijing, China.
  • School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, China.
  • Autolab, Westlake University, Hangzhou, China.
  • School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, China.
  • Medical Artificial Intelligence Laboratory, Westlake University, Hangzhou, China.
  • Department of Electronic Engineering, Tsinghua University, Beijing, China.
  • Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, School of Future Biomedicine, Westlake University, Hangzhou, China. guotiannan@westlake.edu.cn.
  • Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China. guotiannan@westlake.edu.cn.
  • Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, China. guotiannan@westlake.edu.cn.

技术路线

多模态生物数据与机理知识、亚细胞结构、动态状态三类数据支柱 → 细胞复杂性分解为八个功能模块（遗传、代谢、结构系统），各模块为领域AI工具并由大语言模型调度层协调

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/29 12:32
探索相关选题直接查重
查看原文
AI 靶点发现基础研究Nature

Accelerating scientific discovery with Co-Scientist.

Juraj Gottweis、Wei-Hung Weng、Alexander Daryin 等DOI: 10.1038/s41586-026-10644-y
机器学习方法学
157 次引用引用上升

研究旨在加速科学发现，研究人员开发了基于Gemini的多智能体人工智能系统Co-Scientist，用于结构化科学思维与假说生成。该系统根据研究目标和已有科学证据提出可验证的新颖研究假说，其设计包括多智能体架构与异步任务执行框架，以及通过锦标赛式进化过程实现假说生成的自我改进。自动评估显示，测试时计算扩展可持续提升假说质量。研究在药物重定位、新靶点发现和抗菌药物耐药机制解释三个生物医学应用中开展验证；Co-Scientist识别出急性髓系白血病的新药物重定位候选物和协同联合疗法，并经体外实验验证。真实世界验证表明，Co-Scientist有望加速科学发现。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Juraj Gottweis、Wei-Hung Weng、Alexander Daryin、Tao Tu、Petar Sirkovic、Artiom Myaskovsky、Grzegorz Glowaty、Felix Weissenberger、Alessio Orlandi、Dan Popovici、Anil Palepu、Keran Rong、Ryutaro Tanno、Khaled Saab、Fan Zhang、Jacob Blum、Andrew Carroll、Kavita Kulkarni、Nenad Tomašev、Dina Zverinski、Ivor Rendulic、Elahe Vedadi、Florian Hasler、Luka Rimanic、Marina Boia、Ivan Budiselic、Ben Feinstein、Mathias Bellaiche、Tom Sheffer、Jan Freyberg、Jeremy Ratcliff、Ottavia Bertolli、Katherine Chou、Avinatan Hassidim、Burak Gokturk、Amin Vahdat、Yuan Guan、Vikram Dhillon、Eeshit Dhaval Vaishnav、Byron Lee、Tiago R D Costa、José R Penadés、Gary Peltz、Yossi Matias、James Manyika、Demis Hassabis、Yunhan Xu、Pushmeet Kohli、Annalisa Pawlosky、Alan Karthikesalingam、Vivek Natarajan

作者机构

  • Google Cloud AI Research, Zurich, Switzerland. juro@google.com.
  • Google DeepMind, Mountain View, CA, USA. ckbjimmy@google.com.
  • Google Cloud AI Research, Zurich, Switzerland.
  • Google DeepMind, Mountain View, CA, USA.
  • Google Research, Mountain View, CA, USA.
  • Stanford University School of Medicine, Palo Alto, CA, USA.
  • Houston Methodist Hospital, Houston, TX, USA.
  • Sequome, San Francisco, CA, USA.
  • Fleming Initiative and Imperial College London, London, UK.
  • Google DeepMind, Mountain View, CA, USA. pushmeet@google.com.
  • Google Research, Mountain View, CA, USA. apawlosky@google.com.
  • Google DeepMind, Mountain View, CA, USA. alankarthi@google.com.
  • Google DeepMind, Mountain View, CA, USA. natviv@google.com.

技术路线

研究目标与科学证据输入→多智能体（基于Gemini）异步任务执行与锦标赛演化生成/批判/优化假设→自动化评估（测试时算力扩展）→三个生物医学应用（药物重定位、新靶点发现、抗菌素耐药机制解释）→急性髓系白血病候选药物与协同组合的体外实验验证

最近 5 个完整年度引用趋势

0153002021020220202302024302025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/29 12:32
探索相关选题直接查重
查看原文
AI 靶点发现基础研究Nucleic acids research

ARKbase: Antimicrobial Resistance Knowledgebase1.0.

Simran Gambhir、Shweta Pandey、Harsh Bajetha 等DOI: 10.1093/nar/gkaf1307
机器学习方法学
4 次引用趋势数据不足

抗菌药物耐药（AMR）被视为“静默大流行”，是紧迫的全球健康挑战之一。为此，研究者开发了ARKbase，一个面向世界卫生组织细菌重点病原体的整合型、经人工整理并增值的AMR知识库。该知识库包含数据库模块、洞察模块和比较分析模块三个核心部分，提供经整理的数据以及利用定制流程分析所得数据。数据库模块收录经整理的参考基因组、泛基因组及抗菌药物敏感性试验（AST）谱；洞察模块含14个子模块，提供耐药基因、毒力因子、结构注释、宿主—病原互作、生物合成基因簇、已知抗生素、蛋白—蛋白互作、药物靶点、共靶点、药物—靶点互作、机器学习模型及抗生素暴露转录组数据集等深度注释；比较分析模块提供简易界面，用于比较不同重点病原体的耐药基因、毒力因子和药物靶点。数据经质量核查，AST按CLSI标准、基因组序列按EUCAST指南整理，并遵循FAIR数据原则。ARKbase是首个同时聚焦WHO重点病原体和AWaRe分类的综合知识库，有助于以综合证据方法整体理解AMR。

为什么值得关注

AI 辅助短评，基于标题和摘要整理

ARKbase整合了WHO优先病原体的基因组、药敏及多组学注释，模块化设计便于比较ARG、毒力因子和药物靶点。其遵循CLSI、EUCAST和FAIR原则进行数据质控，但摘要未说明任何实验验证或独立基准测试。作为知识库，其实际效用取决于数据覆盖度、更新机制及用户评估，需进一步验证其分析流程的准确性。

查看扩展信息

完整作者

Simran Gambhir、Shweta Pandey、Harsh Bajetha、Jasleen Kaur、Ankita Das、Gnanasekar Pranavathiyani、Rupali Aggarwal、Upasana Maity、Suhani Dange、Vipin Singh、Mayur Zarkar、Raghav Sankhdher、Bhupender Singh、Shivani Seth、Anshu Bhardwaj

作者机构

  • Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh 160036, India.
  • Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
  • UM-DAE Centre for Excellence in Basic Sciences, School of Biological Sciences, University of Mumbai Kalina Campus, Mumbai 400098, India.
  • Life Sciences Department, J.C. Bose University of Science and Technology, YMCA, Sector 6, Mathura road, Faridabad Haryana 121006, India.
  • Independent Researcher, IMTECH Housing, Sector 39A, Chandigarh 160036, India.

技术路线

数据/样本：聚焦WHO细菌优先病原体的参考基因组、泛基因组、AST谱、转录组数据集及多类注释资源（ARG、毒力因子、结构、宿主-病原互作、BGC、药物靶点等） → 分析：使用自建流程进行数据分析和注释，并按CLSI AST标准、EUCAST基因组序列指南及FAIR原则进行质量检查与人工整理

最近 5 个完整年度引用趋势

0110202102022020230202412025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/29 12:32
探索相关选题直接查重
查看原文
AI 靶点发现综述Cancer cell

New horizons at the interface of artificial intelligence and translational cancer research.

Josephine Yates、Eliezer M Van AllenDOI: 10.1016/j.ccell.2025.03.018
肿瘤与癌症机器学习单细胞多组学综述
55 次引用引用上升

人工智能正日益成为分析多组学数据的计算策略应用于癌症研究。单细胞与空间 profiling 技术的进步显著加深了对肿瘤生物学的理解，人工智能方法现被用于加速转化研究，包括靶点发现、生物标志物识别、患者分层和治疗反应预测。然而，人工智能融入临床工作流程仍然有限，既带来挑战也带来机遇。本综述讨论人工智能在多组学分析和转化肿瘤学中的应用，强调其在推进生物学发现和为临床决策提供信息方面的作用。重点关注领域包括细胞异质性、肿瘤微环境相互作用和人工智能辅助诊断。综述探讨了可重复性、人工智能模型可解释性和临床整合等挑战，并关注应对这些障碍的策略。这些进展共同凸显了人工智能与多组学在提升精准肿瘤学和推动癌症护理进步方面的潜力。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Josephine Yates、Eliezer M Van Allen

作者机构

  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Institute for Machine Learning, Department of Computer Science, ETH Zürich, Zurich, Switzerland; ETH AI Center, ETH Zurich, Zurich, Switzerland; Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Medical Sciences, Harvard University, Boston, MA, USA; Parker Institute for Cancer Immunotherapy, Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address: eliezerm_vanallen@dfci.harvard.edu.

技术路线

多组学数据集（单细胞与空间 profiling 数据） → AI 计算分析（细胞异质性、肿瘤微环境互作、靶点发现、生物标志物识别、患者分层、治疗反应预测、AI 辅助诊断）

最近 5 个完整年度引用趋势

0101902021020220202302024192025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/29 12:33
探索相关选题直接查重
查看原文
AI 靶点发现临床研究Nature biotechnology

A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.

Feng Ren、Alex Aliper、Jian Chen 等DOI: 10.1038/s41587-024-02143-0
呼吸系统疾病机器学习成人方法学
347 次引用引用上升

特发性肺纤维化（IPF）是一种侵袭性间质性肺病，死亡率高，既往候选药物靶点未能在临床转化为有效疗法。研究利用预测性人工智能方法识别TRAF2和NCK相互作用激酶（TNIK）为抗纤维化靶点，并据此生成小分子TNIK抑制剂INS018_055。该化合物具有良好成药性，经口服、吸入或局部给药在体内多器官显示抗纤维化活性，并在多项体内研究中验证了抗炎作用。一项随机、双盲、安慰剂对照I期临床试验（NCT05154240）纳入78名健康受试者，验证了其安全性、耐受性和药代动力学；中国另一项I期试验（CTR20221542）也显示可比的安全性和药代动力学特征。从靶点发现到临床前候选药物提名约历时18个月，展示了生成式AI驱动药物发现流程的能力。

为什么值得关注

暂未整理

查看扩展信息

完整作者

Feng Ren、Alex Aliper、Jian Chen、Heng Zhao、Sujata Rao、Christoph Kuppe、Ivan V Ozerov、Man Zhang、Klaus Witte、Chris Kruse、Vladimir Aladinskiy、Yan Ivanenkov、Daniil Polykovskiy、Yanyun Fu、Eugene Babin、Junwen Qiao、Xing Liang、Zhenzhen Mou、Hui Wang、Frank W Pun、Pedro Torres-Ayuso、Alexander Veviorskiy、Dandan Song、Sang Liu、Bei Zhang、Vladimir Naumov、Xiaoqiang Ding、Andrey Kukharenko、Evgeny Izumchenko、Alex Zhavoronkov

作者机构

  • Insilico Medicine Shanghai Ltd., Shanghai, China.
  • Insilico Medicine AI Limited, Abu Dhabi, UAE.
  • Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China.
  • Department of Clinical Pharmacology, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, China.
  • Insilico Medicine US Inc., New York, NY, USA.
  • Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany.
  • Department of Nephrology, University Clinic RWTH Aachen, Aachen, Germany.
  • Insilico Medicine Canada Inc, Montreal, Quebec, Canada.
  • Department of Cancer and Cellular Biology, Lewis Katz School of Medicine, Temple University, PA, USA.
  • Division of Nephrology, Zhongshan Hospital Shanghai Medical College, Fudan University, Shanghai, China.
  • Section of Hematology and Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA.
  • Insilico Medicine AI Limited, Abu Dhabi, UAE. alex@insilico.com.
  • Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China. alex@insilico.com.
  • Insilico Medicine US Inc., New York, NY, USA. alex@insilico.com.
  • Insilico Medicine Canada Inc, Montreal, Quebec, Canada. alex@insilico.com.

技术路线

AI预测靶点→AI驱动生成小分子INS018_055→体内外抗纤维化/抗炎活性评估→随机双盲安慰剂对照I期临床试验（NCT05154240，78名健康受试者）安全性/耐受性/药代动力学验证→中国I期试验（CTR20221542）验证安全性/药代动力学

最近 5 个完整年度引用趋势

0681360202102022020235320241362025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/29 12:33
探索相关选题直接查重
查看原文
页