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Nature Computational Science 2025年 1-14页

作者： Du, Hongru Zhao, Yang Zhao, Jianan Xu, Shaochong Lin, Xihong Chen, Yiran Gardner, Lauren M. Yang, Hao ‘Frank’ Center for Systems Science and Engineering Johns Hopkins University Baltimore MD United States Department of Civil and Systems Engineering Johns Hopkins University Baltimore MD United States Mila - Quebec AI Institute Montreal QC Canada Department of Computer Science Universite de Montréal Montreal QC Canada Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States Department of Statistics Harvard University Cambridge MA United States Department of Electrical and Computer Engineering Duke University Durham NC United States Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD United States

Forecasting the short-term spread of an ongoing disease outbreak poses a challenge owing to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables, and the intersection of public policy and human behavior. Here we introduce PandemicLLM, a framework with multi-modal large language models (LLMs) that reformulates real-time forecasting of disease spread as a text-reasoning problem, with the ability to incorporate real-time, complex, non-numerical information. This approach, through an artificial intelligence–human cooperative prompt design and time-series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial and epidemiological time-series data, and is tested across all 50 states of the United States for a duration of 19 months. PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and shows performance benefits over existing models. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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BanditPAM: almost linear time k-medoids clustering via multi-armed bandits 20

BanditPAM: almost linear time k-medoids clustering via multi...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Mo Tiwari Martin Jinye Zhang James Mayclin Sebastian Thrun Chris Piech Ilan Shomorony Department of Computer Science Stanford University Department of Epidemiology Harvard T.H. Chan School of Public Health Electrical and Computer Engineering University of Illinois at Urbana-Champaign

ISBN: (纸本)9781713829546

Clustering is a ubiquitous task in data science. Compared to the commonly used k-means clustering, k-medoids clustering requires the cluster centers to be actual data points and supports arbitrary distance metrics, which permits greater interpretability and the clustering of structured objects. Current state-of-the-art k-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size n for each iteration, being prohibitively expensive for large datasets. We propose BanditPAM, a randomized algorithm inspired by techniques from multi-armed bandits, that reduces the complexity of each PAM iteration from O(n2) to O(n log n) and returns the same results with high probability, under assumptions on the data that often hold in practice. As such, BanditPAM matches state-of-the-art clustering loss while reaching solutions much faster. We empirically validate our results on several large real-world datasets, including a coding exercise submissions dataset from ***, the 10x Genomics 68k PBMC single-cell RNA sequencing dataset, and the MNIST handwritten digits dataset. In these experiments, we observe that BanditPAM returns the same results as state-of-the-art PAM-like algorithms up to 4x faster while performing up to 200x fewer distance computations. The improvements demonstrated by BanditPAM enable k-medoids clustering on a wide range of applications, including identifying cell types in large-scale single-cell data and providing scalable feedback for students learning computer science online. We also release highly optimized Python and C++ implementations of our algorithm.

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Data mining-based model and risk prediction of colorectal cancer by using secondary health data:A systematic review

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Chinese Journal of Cancer Research 2020年第2期32卷 242-251页

作者： Hailun Liang Lei Yang Lei Tao Leiyu Shi Wuyang Yang Jiawei Bai Da Zheng Ning Wang Jiafu Ji School of Public Administration and Policy Renmin University of ChinaBeijing 100872China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Beijing Office for Cancer Prevention and ControlPeking University Cancer Hospital&InstituteBeijing 100142China Departm ent of Public Policy City University of Hong KongHong Kong SAR999077China Johns Hopkins Primary Care Policy Center BaltimoreMD 21205USA Ddepartment of Neurosurgery Johns Hopkins MedicineBaltimoreMD 21205USA Department of Biostatistics Johns Hopkins Bloomberg School of Public HealthBaltimoreMD 21205USA Department of Computer Science Johns Hopkins Whiting School of EngineeringBaltimoreMD 21205USA Key Laboratory of Carcinogenesis and Translational Research(Ministiy of Education/Beijing) Center of Gastrointestinal SurgeryPeking University Cancer Hospital&InstituteBeijing 100142China

Objective:Prevention and early detection of colorectal cancer(CRC)can increase the chances of successful treatment and reduce *** data mining technologies have been utilized to strengthen the early detection of CRC in primary *** synthesis on the model’s effectiveness is *** systematic review synthesizes studies that examine the effect of data mining on improving risk prediction of ***:The PRISMA framework guided the conduct of this *** obtained papers via Pub Med,Cochrane Library,EMBASE and Google *** appraisal was performed using Downs and Black’s quality *** evaluate the performance of included models,the values of specificity and sensitivity were comparted,the values of area under the curve(AUC)were plotted,and the median of overall AUC of included studies was ***:A total of 316 studies were reviewed for full *** articles were *** studies implement techniques including artificial neural networks,Bayesian networks and decision *** articles reported the overall model ***,the median AUC is 0.8243[interquartile range(IQR):0.8050-0.8886].In the two articles that reported comparison results with traditional models,the data mining method performed better than the traditional models,with the best AUC improvement of 10.7%.Conclusions:The adoption of data mining technologies for CRC detection is at an early *** numbers of included articles and heterogeneity of those studies implied that more rigorous research is expected to further investigate the techniques’effects.

关键词： Systematic review colorectal cancer disease detection data mining

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From 2015 to 2023: How Machine Learning Aids Natural Product Analysis

arXiv

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arXiv 2024年

作者： Shi, Suwen Huang, Ziwei Gu, Xingxin Lin, Xu Zhong, Chaoying Hang, Junjie Lin, Jianli Zhong, Claire Chenwen Zhang, Lin Li, Yu Huang, Junjie Department of Chemistry Boston University BostonMA02215 United States Department of Physics Boston University BostonMA02215 United States College of Professional Studies Northeastern University BostonMA02215 United States Department of Thoracic Surgery The First Affiliated Hospital School of Medicine Zhejiang University Zhejiang Hangzhou China Department of Electrical Engineering and Automation Guangdong Ocean University Guangdong China Cancer Hospital & Shenzhen Hospital Chinese Academy of Medical Sciences Peking Union Medical College Guangdong China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University Beijing China The Jockey Club School of Public Health and Primary Care Faculty of Medicine The Chinese University of Hong Kong Hong Kong Suzhou Industrial Park Monash Research Institute of Science and Technology Suzhou China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine-learning models to yield insightful analytical outcomes. This review delves into the spectrum of computational strategies available for natural product analysis and constructs a research framework for investigating both qualitative and quantitative chemistry problems. Our objective is to present a novel perspective on the symbiosis of machine learning and chemistry, with the potential to catalyze a transformation in the field of natural product analysis. © 2024, CC BY.

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Bandit-PAM: Almost linear time k-medoids clustering via multi-armed bandits

arXiv

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arXiv 2020年

作者： Tiwari, Mo Mayclin, James Piech, Chris Zhang, Martin Jinye Thrun, Sebastian Shomorony, Ilan Department of Computer Science Stanford University T.H. Chan School of Public Health Harvard University Electrical and Computer Engineering University of Illinois at Urbana-Champaign

Clustering is a ubiquitous task in data science. Compared to the commonly used k-means clustering algorithm, k-medoids clustering algorithms require the cluster centers to be actual data points and support arbitrary distance metrics, allowing for greater interpretability and the clustering of structured objects. Current state-of-the-art k-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size n for each iteration, being prohibitively expensive for large datasets. We propose Bandit-PAM, a randomized algorithm inspired by techniques from multi-armed bandits, that significantly improves the computational efficiency of PAM. We theoretically prove that BanditPAM reduces the complexity of each PAM iteration from O(n2) to O(n log n) and returns the same results with high probability, under assumptions on the data that often hold in practice. We empirically validate our results on several large-scale real-world datasets, including a coding exercise submissions dataset from ***, the 10x Genomics 68k PBMC single-cell RNA sequencing dataset, and the MNIST handwritten digits dataset. We observe that Bandit-PAM returns the same results as PAM while performing up to 200x fewer distance computations. The improvements demonstrated by Bandit-PAM enable k-medoids clustering on a wide range of applications, including identifying cell types in large-scale single-cell data and providing scalable feedback for students learning computer science online. We also release Python1 and C++2 implementations of our algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges

arXiv

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arXiv 2025年

作者： Zhang, Yuan Zhang, Xinfeng Qi, Xiaoming Wu, Xinyu Chen, Feng Yang, Guanyu Fu, Huazhu Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications [Southeast University Ministry of Education Nanjing China School of Biomedical Engineering Tsinghua University Beijing China Department of Biomedical Engineering Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Information Science and Engineering Southeast University Nanjing China Department of Biostatistics Center for Global Health School of Public Health Nanjing Medical University Nanjing China Institute of High-Performance Computing Agency for Science Technology and Research Singapore

Generative modeling has emerged as a promising direction in computational pathology, offering capabilities such as data-efficient learning, synthetic data augmentation, and multimodal representation across diverse diagnostic tasks. This review provides a comprehensive synthesis of recent progress in the field, organized into four key domains: image generation, text generation, multimodal image–text generation, and other generative applications, including spatial simulation and molecular inference. By analyzing over 150 representative studies, we trace the evolution of generative architectures—from early generative adversarial networks to recent advances in diffusion models and foundation models with generative capabilities. We further examine the datasets and evaluation protocols commonly used in this domain and highlight ongoing limitations, including challenges in generating high-fidelity whole slide images, clinical interpretability, and concerns related to the ethical and legal implications of synthetic data. The review concludes with a discussion of open challenges and prospective research directions, with an emphasis on developing unified, multimodal, and clinically deployable generative systems. This work aims to provide a foundational reference for researchers and practitioners developing and applying generative models in computational pathology. © 2025, CC BY-NC-ND.

关键词： Generative adversarial networks

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Eight Become One: the EURECA-PRO University Network

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BHM Berg- und Huttenmannische Monatshefte 2022年第10期167卷 462-471页

作者： Kircher, Volkmar Feiel, Susanne Bongaerts, Jan C. Sishchuk, Julia Drebenstedt, Carsten Lagoudakis, Michail Fragkedaki, Konstantina García-Llamas, Paula Suárez-Corona, Adriana Fernández-Raga, María Baelo-Álvarez, Roberto Górski, Marcin Pawelczyk, Marek Hochstein, Synnöve Walter, Stefanie Speck, Maria Ravas, Oana Van Hellemont, Elke Vancauwenbergh, Sadia Resources Innovation Center Leoben Montanuniversität Leoben Leoben Austria Public Relations Office International Relations and University Cooperation Montanuniversität Leoben Leoben Austria Faculty of Business Administration Technische Universität Bergakademie Freiberg Freiberg Germany EURECA-PRO Head of work package education and studies Technische Universität Bergakademie Freiberg Freiberg Germany EURECA-PRO Head of educational council Technische Universität Bergakademie Freiberg Freiberg Germany School of Electrical and Computer Engineering Technical University of Crete Chania Greece EURECA-PRO European University Technical University of Crete Chania Greece Department of General and Specific Didactics and Educational Theory University of León Leon24071 Spain Department of Mathematical Sciences Universidad de León León24071 Spain Department of Applied Physics University of León León24071 Spain Faculty of Civil Engineering Silesian University of Technology Gliwice Poland Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Fakultät Medien Hochschule Mittweida Mittweida Germany International Relation Office University of Petroşani Petroşani Romania Rector’s Office Hasselt University Hasselt Belgium Directorate Research Library and Internationalisation Hasselt University Hasselt Belgium

The United Nations Sustainable Development Goal 12 is the foundation of the European University on Responsible Consumption and Production consortium (EURECA-PRO). This article introduces the eight EURECA-PRO partner universities: Montanuniversität Leoben (Austria), Technische Universität Bergakademie Freiberg (Germany), Technical University of Crete (Greece), University of León (Spain), Silesian University of Technology (Poland), Mittweida University of Applied Sciences (Germany), University of Petroşani (Romania), and Hasselt University (Belgium). In addition, each university’s role within the alliance and unique research and study programmes are outlined. The synergy created by EURECA-PRO enables the pursuit of an ambitious research agenda with five research "Lighthouse Missions" as well as the implementation of joint study programmes. © 2022, The Author(s).

关键词： Sustainable development

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Evaluating Offshore Electricity Market Design Considering Endogenous Infrastructure Investments: Zonal or Nodal?

arXiv

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arXiv 2024年

作者： Kenis, Michiel Dvorkin, Vladimir Schittekatte, Tim Bruninx, Kenneth Delarue, Erik Botterud, Audun Leuven3001 Belgium The EnergyVille Thor Park 8310 Genk3600 Belgium Boeretang 200 MolB-2400 Belgium The Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States The MIT Sloan School of Management Massachusetts Institute of Technology CambridgeMA02142 United States The Florence School of Regulation European University Institute FI Firenze50133 Italy The Faculty of Technology Policy and Management TU Delft Delft2628 Netherlands The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02142 United States

policy makers are formulating offshore energy infrastructure plans, including wind turbines, electrolyzers, and HVDC transmission lines. An effective market design is crucial to guide cost-efficient investments and dispatch decisions. This paper jointly studies the impact of offshore market design choices on the investment in offshore electrolyzers and HVDC transmission capacity. We present a bilevel model that incorporates investments in offshore energy infrastructure, day-ahead market dispatch, and potential redispatch actions near real-time to ensure transmission constraints are respected. Our findings demonstrate that full nodal pricing, i.e., nodal pricing both onshore and offshore, outperforms the onshore zonal combined with offshore nodal pricing or offshore zonal layouts. While combining onshore zonal with offshore nodal pricing can be considered as a second-best option, it generally diminishes the profitability of offshore wind farms. However, if investment costs of offshore electrolyzers are relatively low, they can serve as catalysts to increase the revenues of the offshore wind farms. This study contributes to the understanding of market designs for highly interconnected offshore power systems, offering insights into the impact of congestion pricing methodologies on investment decisions. Besides, it is useful towards understanding the interaction of offshore loads like electrolyzers with financial support mechanisms for offshore wind farms. © 2024, CC BY.

关键词： Offshore wind farms

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Traditional and advanced neuroimaging contributions to the diagnosis and differential diagnosis of central nervous system lymphoma patients visiting a comprehensive medical center

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Open Neuroimaging Journal 2020年 13卷 6-15页

作者： Dirani, Ali El Hachem, Zahraa Mohanna, Assaad Zaylaa, Amira J. Department of Hematology Oncology Faculty of Medical Sciences Lebanese University Beirut Lebanon Department of Neurology Centre Hospitalier de Calais 1601 Boulevard des Justes Calais 62100 France Neuroscience Research Center Faculty of Medical Sciences Lebanese University Beirut Lebanon Faculty of Public Health-I Lebanese University Beirut Lebanon Department of Electrical and Computer Engineering Beirut Arab University Lebanon

Introduction: The diagnosis of Central Nervous System Lymphoma, especially the Primary Central Nervous System Lymphoma is carried out based on brain imaging, thus avoiding an unnecessary extend of surgery. But the traditional imaging techniques, such as Computed Tomography and Magnetic Resonance Imaging, were not satisfactory. Aims: This study was conducted to characterize the spectrum of advanced Neuroimaging, such as the advanced Magnetic Resonance Imaging features in the Central Nervous System Lymphoma patients in a comprehensive medical center in Lebanon, and compare them to what has been described in the literature review. Methods: It is a retrospective exploratory study of the clinical data and imaging features for patients admitted to the emergency and radiology departments with ages above 10 years, and who were diagnosed histopathologically with intracranial lymphoma. This study may be the first to make a Radiological evaluation of Central Nervous System Lymphoma on the local population of patients over 9 years . Results: Results showed that the study of the Computed Tomography and Magnetic Resonance Imaging data of 10 immunocompetent patients with Central Nervous System Lymphoma concurs with the previously described patient populations, except for the gender parameter. Tumors were mostly presented in the fifth or Sixth decade and they could be solitary or multi-focal. Lesions were typically located Preprint submitted to The Open Neuroimaging Journal May 14, 2020 in the supratentorial compartment. On the brain Computed Tomography, the lesions were hyperdense, and in pre-contrast Magnetic Resonance images, the lesions appeared hypointense on T1 and hyperintense on T2-Weighted images, but hypointense with respect to the grey matter. The lesions were also surrounded with a mild to moderate edema as compared to other intracranial neoplasms, such as glioblastomas. Evaluation results showed that on post-contrast Magnetic Resonance images, the majority of lesions e

关键词： Advanced neuroimaging Central nervous system lymphoma Computed tomography Imaging parameters Immunocompetence Magnetic resonance imaging Statistical evaluation

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The Impact of Heterogeneous Shared Leadership in Scientific Teams

arXiv

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arXiv 2023年

作者： Xu, Huimin Liu, Meijun Bu, Yi Sun, Shujing Zhang, Yi Zhang, Chenwei Acuna, Daniel E. Gray, Steven Meyer, Eric Ding, Ying School of Information University of Texas at Austin AustinTX78701 United States Institute for Global Public Policy Fudan University Shanghai200433 China Department of Information Management Peking University Beijing100871 China School of Management University of Texas at Dallas DallasTX75080 United States Faculty of Engineering and IT University of Technology SydneyNSW2007 Australia Faculty of Education University of Hong Kong 999077 Hong Kong Department of Computer Science University of Colorado at Boulder Boulder Syracuse United States School of Business University of Texas at Austin AustinTX78712 United States

Leadership is evolving dynamically from an individual endeavor to shared efforts. This paper aims to advance our understanding of shared leadership in scientific teams. We define three kinds of leaders, junior (10-15), mid (15-20), and senior (20+) based on career age. By considering the combinations of any two leaders, we distinguish shared leadership as "heterogeneous" when leaders are in different age cohorts and "homogeneous" when leaders are in the same age cohort. Drawing on 1,845,351 CS, 254,039 Sociology, and 193,338 Business teams with two leaders in the OpenAlex dataset, we identify that heterogeneous shared leadership brings higher citation impact for teams than homogeneous shared leadership. Specifically, when junior leaders are paired with senior leaders, it significantly increases team citation ranking by 1-2%, in comparison with two leaders of similar age. We explore the patterns between homogeneous leaders and heterogeneous leaders from team scale, expertise composition, and knowledge recency perspectives. Compared with homogeneous leaders, heterogeneous leaders are more adaptive in large teams, have more diverse expertise, and trace both the newest and oldest references. © 2023, CC BY.

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