检索结果-内蒙古大学图书馆

Artificial intelligence for modelling infectious disease epidemics

Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FedScore: A Privacy-Preserving Framework for Federated Scoring System Development

arXiv

引用

arXiv 2023年

作者： Li, Siqi Ning, Yilin Ong, Marcus Eng Hock Chakraborty, Bibhas Hong, Chuan Xie, Feng Yuan, Han Liu, Mingxuan Buckland, Daniel M. Chen, Yong Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Health Services Research Centre Singapore Health Services Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Department of Emergency Medicine Duke University School of Medicine DurhamNC United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States Institute of Data Science National University of Singapore Singapore Singapore

Objective We propose FedScore, a privacy-preserving federated learning framework for scoring system generation across multiple sites to facilitate cross-institutional collaborations. Materials and Methods The FedScore framework includes five modules: federated variable ranking, federated variable transformation, federated score derivation, federated model selection and federated model evaluation. To illustrate usage and assess FedScore’s performance, we built a hypothetical global scoring system for mortality prediction within 30 days after a visit to an emergency department using 10 simulated sites divided from a tertiary hospital in Singapore. We employed a preexisting score generator to construct 10 local scoring systems independently at each site and we also developed a scoring system using centralized data for comparison. Results We compared the acquired FedScore model’s performance with that of other scoring models using the receiver operating characteristic (ROC) analysis. The FedScore model achieved an average area under the curve (AUC) value of 0.763 across all sites, with a standard deviation (SD) of 0.020. We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models. Conclusion This study demonstrates that FedScore is a privacy-preserving scoring system generator with potentially good generalizability. © 2023, CC BY.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

OPTIMAL ADAPTIVE SMART DESIGNS WITH BINARY OUTCOMES

arXiv

引用

arXiv 2023年

作者： Ghosh, Rik Chakraborty, Bibhas Nahum-Shani, Inbal Patrick, Megan E. Ghosh, Palash Department of Mathematics Indian Institute of Technology Assam Guwahati India Centre for Quantitative Medicine Duke-NUS Medical School National University of Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Institute for Social Research University of Michigan United States Department of Mathematics Jyoti and Bhupat Mehta School of Health Sciences and Technology Indian Institute of Technology Assam Guwahati India

In a sequential multiple-assignment randomized trial (SMART), a sequence of treatments is given to a patient over multiple stages. In each stage, randomization may be done to allocate patients to different treatment groups. Even though SMART designs are getting popular among clinical researchers, the methodologies for adaptive randomization at different stages of a SMART are few and not sophisticated enough to handle the complexity of optimal allocation of treatments at every stage of a trial. Lack of optimal allocation methodologies can raise serious concerns about SMART designs from an ethical point of view. In this work, we develop an optimal adaptive allocation procedure to minimize the expected number of treatment failures for a SMART with a binary primary outcome. Issues related to optimal adaptive allocations are explored theoretically with supporting simulations. The applicability of the proposed methodology is demonstrated using a recently conducted SMART study named M-Bridge for developing universal and resource-efficient dynamic treatment regimes (DTRs) for incoming first-year college students as a bridge to desirable treatments to address alcohol-related risks. © 2023, CC BY-NC-SA.

关键词： Bridges

来源：评论

学校读者我要写书评

暂无评论

Digitizing Farmers' Land data Collection Systems in Indonesia with the Use of Tani Millenial Apps

Digitizing Farmers' Land Data Collection Systems in Indonesi...

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mohammad Prasanto Bimantio Dian Pratama Putra Teddy Suparyanto Amallia Ferhat Nanda Satya Nugraha Alam Ahmad Hidayat Bens Pardamean Department of Food and Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Agricultural Socio-Economics Institute of Agriculture STIPER Yogyakarta Indonesia Department of Forestry Institute of Agriculture STIPER Yogyakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

In this era of digital world information, farmers face challenges about information management of huge data and the complexity of processes in precision farming. The data collection carried out so far can cause several difficulties, especially if the data is needed by the government or academics to support decision-making. Agricultural data collection applications only cover 12% of the market share of Android-based agriculture-based applications. In this study, Tani Milenial application is present as an alternative to support agricultural data collection activities in Indonesia. This application encourages farmers to be active and contribute independently to their role as subjects of agricultural development and is accustomed to digitally recording their agricultural activities. In this developed application system, the community, especially farmers and farmer groups, will find it easier to get information about agricultural potential data around where they carry out agricultural activities and can access this information online. Tani Milenial works as a guide to help farmers and transform agriculture but still have some gaps which should be checked and removed. The data collected can be used by various parties for research and decision-making. The app can be further improved to develop new features related to education, market information, and goods.

关键词： Decision making Education Government Transforms data collection Agriculture Recording

来源：评论

学校读者我要写书评

暂无评论

Genome data mining approach to identify potential protein in crop plants

引用

AIP Conference Proceedings 2024年第1期3026卷

作者： Joko Pebrianto Trinugroho Faisal Asadi Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

There is a need to produce more crop plants to meet the future global demand. However, the climate change has affected the global crop yield. Consequently, finding an alternative approach to improve crop yield becomes essential. The development of sequencing techniques, as well as information technologies, have enabled us to perform genome data mining. Using genome data mining approach, it is possible to identify or discover a protein which has a particular characteristic. This study aims to identify a protein, which could potentially improve crop yield, using genome data mining approach. D1 protein was used as the target, as this protein is highly involved in photosynthesis. Then, protein sequences of various crop plants were collected from biological database. After conducting data trimming and filtering, sequence analysis was performed. The analysis was used to construct phylogenetic tree and create a 3D protein model. Sequence analysis displayed variation in amino acid sequence in D1 protein. Protein modelling located the variations, which scattered within D1 protein. Furthermore, we highlighted the amino acid residues that are the targets for genetic engineering. The research findings may provide a reference to improve crop production through genome mining approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Unique,Persistent,Resolvable:Identifiers as the Foundation of FAIR

引用

data Intelligence 2020年第1期2卷 30-39,302页

作者： Nick Juty Sarala M.Wimalaratne Stian Soiland-Reyes John Kunze Carole A.Goble Tim Clark Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK California Digital Library OaklandCalifornia 94612-2901USA Data Science Institute University of VirginiaCharlottesvilleVA 22903-1738USA

The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ***,globally unique identifiers,resolvable on the Web,and associated with a set of additional descriptive metadata,are foundational to FAIR *** we describe some basic principles and exemplars for their design,use and orchestration with other system elements to achieve FAIRness for digital research objects.

关键词： Identifiers metadata findability FAIR data data infrastructures

来源：评论

学校读者我要写书评

暂无评论

Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models

arXiv

引用

arXiv 2024年

作者： Sankar, Baradwaj Simha Gilliland, Destiny Rincon, Jack Hermjakob, Henning Yan, Yu Adam, Irsyad Lemaster, Gwyneth Wang, Dean Watson, Karol Bui, Alex Wang, Wei Ping, Peipei Department of Physiology University of California Los AngelesCA United States NIH BRIDGE2AI Center UCLA NHLBI Integrated Cardiovascular Data Science Training Program at UCLA Los AngelesCA United States Cambridge United Kingdom Bioinformatics IDP University of California Los AngelesCA United States University of California Los AngelesCA United States Medical Informatics Home Area University of California Los AngelesCA United States Department of Computer Science University of California Los AngelesCA United States

Foundational Models (FMs) are gaining increasing attention in the biomedical AI ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders-especially those involved in or affected by these clinical and translational applications-are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predicting drug response through tumor deconvolution by cancer cell lines

引用

Patterns 2024年第4期5卷 100949页

作者： Hsu, Yu-Ching Chiu, Yu-Chiao Lu, Tzu-Pin Hsiao, Tzu-Hung Chen, Yidong Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei115 Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei100 Taiwan Greehey Children's Cancer Research Institute University of Texas Health San Antonio San AntonioTX78229 United States Department of Medicine School of Medicine University of Pittsburgh PittsburghPA15261 United States UPMC Hillman Cancer Center University of Pittsburgh PittsburghPA15232 United States Department of Medical Research Taichung Veterans General Hospital Taichung40705 Taiwan Department of Population Health Sciences University of Texas Health San Antonio San AntonioTX78229 United States

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing. © 2024 The Authors

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Study of City Crimes with Smart Solutions Integrating into Smart Cities, Internet of Things (IoT), and Cybersecurity Systems

Study of City Crimes with Smart Solutions Integrating into S...

引用

Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Al-Siyam Rahman Md. Sadik Tasrif Anubhove Mohammad Zeyad S.M. Masum Ahmed Md. Abul Ala Walid Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh (ABCD Laboratory Bangladesh) Chattogram Bangladesh Erasmus+ Joint MSC in Cybersecurity Faculty of Sciences Université libre de Bruxelles (ULB) Brussels Belgium Avenues Department of Urban Engineering Universite de Technologie de Compiègne (UTC) Compiègne France Centre for Research on Geography Resources Environment Energy & Networks (Green) Bocconi University Milano Italy Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh

The development of smart cities enhances the quality of life for many people by promoting digital interconnection, which encourages dependability and accessibility in cities. Cities have multiple security issues to deal with, as well as kidnapping, rape, child abuse, and violence, completely of which stand on the rise globally. This kind of crime constantly disturbs the victim not only physically and mentally but also forces them to attempt suicide, which can ruin the happiness of many families in a city. Conventional methods are insufficient to safeguard a city from crime and provide a secure way of life for everyone, which is depraved for society and their day-to-day activities. However, this paper, first analyzes the different types of criminal activities in Bangladesh, such as kidnapping, rape, murder, and child abuse, and here offer various technological solutions, to protect cities against the worst crimes, using technologies like cybersecurity and the Internet of Things (IoT) based technologies. With the help of various technological solutions, a city becomes safer while also achieving Sustainable Development Goals (SDG's) number 11, which promotes sustainable cities and communities. This paper clarifies how to reduce criminal activities by making use of a range of technologies, which benefit the country and improve people's quality of life.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prenatal exposure to parabens in relation to childhood obesity and the gut microbiome

引用

Taiwan Journal of Public Health 2023年第1期42卷 62-74页

作者： Chen, Ying Kuo, Chin-Chi Chen, Ting-Wen Wang, Shu-Li Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Big Data Center China Medical University Hospital Taichung Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan National Institute of Environmental Health Sciences National Health Research Institutes No. 35 Keyan Rd Zhunan Town Miaoli Taiwan Department of Public Health National Defence Medical Centre Taipei Taiwan

Objectives: Parabens, which are common antimicrobial preservatives, are considered to be endocrine-disrupting chemicals. Previous studies regarding maternal exposure to paraben have shown that disturbances in physiological endocrine and metabolic signals during early childhood may lead to the long-term adverse health effects in children. We investigated the effects of maternal exposure to paraben on childhood obesity and gut microbiome diversity. Methods: We collected the data on 284 maternalinfant pairs from the central region of Taiwan Maternal- Infant Cohort Study. Liquid chromatography-tandem mass spectrometry was used to detect 4 common parabens, including methyl-paraben, ethyl-paraben, propyl-paraben, and butyl-paraben in maternal urinary samples. In addition, shotgun metagenomics sequencing was used to analyze 98 children’s fecal samples. Results: Greater maternal exposure to paraben was observed in boys who were significantly shorter and with wider chest circumferences and in girls with higher body mass index. Increased maternal exposure to paraben significantly reduced the abundance of several species, such as Barnesiella, Bacteroides uniformis, and Eubacterium rectale, but increased the abundance of several opportunistic pathogens, which interfered with the regulation of energy consumption. Conclusions: Maternal exposure to paraben promoted the development of obesity in girls and disrupted the diversity of gut microbial community. Maternal exposure to paraben may promote obesity in children at preschooler age. In addition, additional studies on the effects of the composition of the gut microbiome after puberty are required. © 2023, Taiwan Public Health Association. All rights reserved.

关键词： childhood obesity gut microbiome parabens prenatal exposure

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：