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AI-BASED FAST AND SCALABLE IMAGE SEARCH FOR RARE DISEASE DIAGNOSIS

Journal of Pathology Informatics 2022年 13卷 100046-100046页

作者： Chen, Chengkuan Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Schaumberg, Andrew J. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School Boston MA USA Cancer Program Broad Institute of Harvard and MIT Cambridge MA USA Cancer Data Science Dana-Farber Cancer Institute Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA

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Author Correction: A machine learning approach to leveraging electronic health records for enhanced omics analysis

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Nature Machine Intelligence 2025年第4期7卷 673页

作者： Samson J. Mataraso Camilo A. Espinosa David Seong S. Momsen Reincke Eloise Berson Yeasul Kim Marc Ghanem Chi-Hung Shu Tomin James Sayane Shome Ina A. Stelzer Dorien Feyaerts Martin S. Angst Brice Gaudilliere Nima Aghaeepour Jonathan D. Reiss Ronald J. Wong Gary M. Shaw David K. Stevenson Yuqi Tan Department of Anesthesiology Perioperative and Pain Medicine Stanford University School of Medicine Stanford CA USA Department of Pediatrics Stanford University School of Medicine Stanford CA USA Department of Biomedical Data Science Stanford University School of Medicine Stanford CA USA Immunology Program Stanford University School of Medicine Stanford CA USA Medical Scientist Training Program Stanford University School of Medicine Stanford CA USA Department of Pathology Stanford University School of Medicine Stanford CA USA Department of Pathology University of California San Diego La Jolla CA USA Department of Microbiology and Immunology Stanford University School of Medicine Stanford CA USA

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276MO Epidemiology, treatment (tx) patterns, and clinical outcomes of patients (pts) with locally advanced or metastatic urothelial cancer (la/mUC) in Taiwan: A retrospective cohort study

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Annals of Oncology 2024年 35卷 S1511-S1511页

作者： C-T. Wu L. Young A. Lee M-Y. Ku Y-C. Hsieh H-W. Lin K-L. Lin K-P. Chou M. Kearney L-N. Chien Department of Urology Chang Gung Memorial Hospital Taoyuan City Taiwan Global Medical Unit Oncology Merck Pte. Ltd. Singapore Science Park Singapore Medical Affairs Oncology Merck Ltd. Taipei Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei Taiwan Program in Medical Neuroscience Taipei Medical University Taipei Taiwan Real-world Solutions IQVIA Solutions Taiwan Ltd. Taipei Taiwan Global Value Demonstration Market Access and Pricing Biopharma Global Operations Merck Healthcare KGaA Darmstadt Germany Institute of Health and Welfare Policy College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

arXiv

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arXiv 2023年

作者： Takaba, Kenichiro Pulido, Iván Behara, Pavan Kumar Cavender, Chapin E. Friedman, Anika J. Henry, Michael M. MacDermott-Opeskin, Hugo Iacovella, Christopher R. Nagle, Arnav M. Payne, Alexander Matthew Shirts, Michael R. Mobley, David L. Chodera, John D. Wang, Yuanqing Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Pharmaceuticals Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Center for Neurotherapeutics Department of Pathology and Laboratory Medicine University of California IrvineCA92697 United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Open Molecular Software Foundation DavisCA95618 United States Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Pharmaceutical Sciences University of California IrvineCA92697 United States Tri-Institutional Ph.D. Program in Chemical Biology Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University New YorkNY10004 United States

The development of reliable and extensible molecular mechanics (MM) force fields—fast, empirical models characterizing the potential energy surface of molecular systems—is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. © 2023, CC BY.

关键词： Quantum chemistry

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Validation of the CaRdiac Arrest Survival Score (CRASS) for predicting good neurological outcome after out-of-hospital cardiac arrest in an Asian emergency medical service system

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Resuscitation 2022年第0期176卷 42-50页

作者： Liu, Nan Wnent, Jan Lee, Jin Wee Ning, Yilin Ho, Andrew Fu Wah Siddiqui, Fahad Javaid Lim, Shir Lynn Chia, Michael Yih-Chong Tiah, Ling Mao, Desmond Ren-Hao Gräsner, Jan-Thorsten Ong, Marcus Eng Hock Gan, Han Nee Cheah, Si Oon Ng, Wei Ming Tay, Wei Ling Leong, Benjamin S.H. Nadarajan, Gayathri Doctor, Nausheen Edwin Tham, Lai Peng Arulanandam, Shalini Health Services and Systems Research Duke-NUS Medical School Singapore Centre for Quantitative Medicine Duke-NUS Medical School Singapore Health Services Research Centre Singapore Health Services Singapore SingHealth AI Health Program Singapore Health Services Singapore Institute of Data Science National University of Singapore Singapore Institute for Emergency Medicine University Hospital Schleswig-Holstein Kiel Germany Department of Anaesthesiology and Intensive Care Medicine University Hospital Schleswig-Holstein Kiel Germany School of Medicine University of Namibia Windhoek Namibia Department of Emergency Medicine Singapore General Hospital Singapore Prehospital Emergency Research Centre Duke-NUS Medical School Singapore Department of Cardiology National University Heart Centre Singapore Department of Medicine Yong Loo Lin School of Medicine Singapore Emergency Department Tan Tock Seng Hospital Singapore Accident & Emergency Department Changi General Hospital Singapore Acute and Emergency Care Khoo Teck Puat Hospital Singapore Changi General Hospital Singapore Urgent Care Clinic International Singapore Ng Teng Fong General Hospital Singapore National University Hospital Singapore Singapore General Hospital Singapore Sengkang General Hospital Singapore KK Women's & Children's Hospital Singapore Previously Chief Medical Officer at Singapore Civil Defence Force Singapore

Background: Survival with favorable neurological outcomes is an important indicator of successful resuscitation in out-of-hospital cardiac arrest (OHCA). We sought to validate the CaRdiac Arrest Survival Score (CRASS), derived using data from the German Resuscitation Registry, in predicting the likelihood of good neurological outcomes after OHCA in Singapore. Methods: We conducted a retrospective population-based validation study among EMS-attended OHCA patients (≥18 years) in Singapore, using data from the prospective Pan-Asian Resuscitation Outcomes Study registry. Good neurological outcome was defined as a cerebral performance category of 1 or 2. To evaluate the CRASS score in light of the difference in patient characteristics, we used the default constant coefficient (0.8) and the adjusted coefficient (0.2) to calculate the probability of good neurological outcomes. Results: Out of 11,404 analyzed patients recruited between April 2010 and December 2018, 260 had good and 11,144 had poor neurological function. The CRASS score demonstrated good discrimination, with an area under the curve of 0.963 (95% confidence interval: 0.952–0.974). Using the default constant coefficient of 0.8, the CRASS score consistently overestimated the predicted probability of a good outcome. Following adjustment of the coefficient to 0.2, the CRASS score showed improved calibration. Conclusion: CRASS demonstrated good discrimination and moderate calibration in predicting favorable neurological outcomes in the validation Singapore cohort. Our study established a good foundation for future large-scale, cross-country validations of the CRASS score in diverse sociocultural, geographical, and clinical settings. © 2022 Elsevier B.V.

关键词： Asia Cardiac arrest Emergency medical service Neurological outcome Score Survival

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Detecting anomalous citation groups in journal networks

arXiv

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arXiv 2020年

作者： Kojaku, Sadamori Livan, Giacomo Masuda, Naoki Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Computer Science University College London LondonWC1E 6EA United Kingdom Systemic Risk Centre London School of Economics and Political Science LondonWC2A 2AE United Kingdom Department of Mathematics University at Buffalo State University of New York BuffaloNY United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-2900 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a "citation cartel" of journals. "Citation cartel" behavior has become increasingly common in recent years, with several instances being reported. Here, we propose an algorithm—-named CIDRE—to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended from Journal Citation Reports due to anomalous citation behavior in the year of suspension or in advance. Furthermore, CIDRE detects many new anomalous groups, where the impact factors of the member journals are lifted substantially higher by the citations from other member journals. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate. Copyright © 2020, The Authors. All rights reserved.

关键词： Publishing

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Core variables for real-world clinicogenomic data collection in precision oncology

ESMO Real World Data and Digital Oncology

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ESMO Real World data and Digital Oncology 2025年 7卷 100117-100117页

作者： R. Dienstmann A. Hackshaw J.-Y. Blay C. Le Tourneau OC Precision Medicina Oncoclínicas & Co São Paulo Brazil PhD Program Doctoral School University of Vic—Central University of Catalonia Barcelona Spain Oncology Data Science Vall d’Hebron Institute of Oncology Barcelona Spain Cancer Research UK and UCL Cancer Trials Centre University College London London UK Centre Léon Bérard and Université Claude Bernard Lyon France Institut Curie Department of Drug Development and Innovation (D3i) Paris-Saclay University Paris France

Background Precision oncology evidence gaps may be bridged using real-world clinicogenomic data; however, current limitations compromise real-world data collection and evidence generation. We aimed to define a set of precision oncology core variables that can aid in fit-for-purpose real-world data collection to be used for a range of purposes and by various stakeholders based on the approach adopted when designing the WAYFIND-R registry (NCT04529122). Materials and methods A panel of precision oncology experts created a list of variables based on standard dictionaries and vocabularies, which was aligned with the European Medical Agency’s draft guidelines for registry-based studies. A list of core variables was selected from the initial broad list of variables. Results The initial overall list (∼500 variables) was consolidated to ∼150 variables that covered the entirety of the patient journey in oncology. The panel gave highest priority to patient demographics, socioeconomic information and comorbidities, cancer details, molecular information, particularly predictive biomarkers in routine use and next-generation sequencing technical aspects, systemic cancer therapies and other treatments, outcome assessments and survival outcomes. Conclusions The core oncology variables represent a harmonized list of key clinicogenomic data elements that can be collected during a patient’s journey in oncology and leveraged for their primary intended purpose of molecular tumor board decision making, and might be used for further secondary research purposes, if legal requirements allow. Definition of this list of core variables will help generate evidence from research- or regulatory-oriented precision oncology studies by ensuring synergy, dataset connectivity, and interoperability across different data.

关键词： precision oncology real-world data solid tumors next-generation sequencing molecular tumor boards

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Electronic and Magnetic Properties of Topological Semimetal Candidate NdSbTe

arXiv

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arXiv 2020年

作者： Pandey, Krishna Basnet, Rabindra Wegner, Aaron Acharya, Gokul Un Nabi, Md Rafique Liu, Jiangwei Wang, Jian Takahashi, Yukiko Da, Bo Hu, Jin Materials Science and Engineering Program Institute for Nanoscience and Engineering University of Arkansas FayettevilleAR72701 United States Department of Physics University of Arkansas FayettevilleAR72701 United States Research Center for Functional Materials National Institute for Materials Science TsukubaIbaraki305-0047 Japan Institute for Materials Research Center for Spintronics Research Network Tohoku University Sendai980-8577 Japan Research Center for Magnetic and Spintronic Materials National Institute for Materials Science Sengen 1-2-1 TsukubaIbaraki305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science TsukubaIbaraki305-0044 Japan

ZrSiS-type materials represent a large material family with unusual coexistence of topological nonsymmorphic Dirac fermions and nodal-line fermions. As a special group of ZrSiS-family, LnSbTe (Ln = Lanthanide rare earth) compounds provide a unique opportunity to explore new quantum phases due to the intrinsic magnetism induced by Ln. Here we report the single crystal growth and characterization of NdSbTe, a previously unexplored LnSbTe compound. NdSbTe has an antiferromagnetic ground state with field-driven metamagnetic transitions similar to other known LnSbTe, but exhibits distinct enhanced electronic correlations characterized by large a Sommerfeld coefficient of 115 mJ/mol K2, which is the highest among the known LnSbTe compounds. Furthermore, our transport studies have revealed the coupling with magnetism and signatures of Kondo localization. All these findings establish NdSbTe as a new platform for observing novel phenomena arising from the interplay between magnetism, topology, and electron correlations. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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