检索结果-内蒙古大学图书馆

A future for the world's children? A WHO–UNICEF–Lancet Commission

The Lancet 2020年第10224期395卷 605-658页

作者： Clark, Helen Coll-Seck, Awa Marie Banerjee, Anshu Peterson, Stefan Dalglish, Sarah L Ameratunga, Shanthi Balabanova, Dina Bhan, Maharaj Kishan Bhutta, Zulfiqar A Borrazzo, John Claeson, Mariam Doherty, Tanya El-Jardali, Fadi George, Asha S Gichaga, Angela Gram, Lu Hipgrave, David B Kwamie, Aku Meng, Qingyue Mercer, Raúl Narain, Sunita Nsungwa-Sabiiti, Jesca Olumide, Adesola O Osrin, David Powell-Jackson, Timothy Rasanathan, Kumanan Rasul, Imran Reid, Papaarangi Requejo, Jennifer Rohde, Sarah S Rollins, Nigel Romedenne, Magali Singh Sachdev, Harshpal Saleh, Rana Shawar, Yusra R Shiffman, Jeremy Simon, Jonathon Sly, Peter D Stenberg, Karin Tomlinson, Mark Ved, Rajani R Costello, Anthony The Helen Clark Foundation Auckland New Zealand Partnership for Maternal Newborn & Child Health Geneva Switzerland Senegal Presidency Dakar Senegal Department of Maternal Newborn Child and Adolescent Health WHO Geneva Switzerland Department of Health Systems Governance and Financing WHO Geneva Switzerland Health Division HIV Data and Analytics Division UNICEF New York NY United States HIV Data and Analytics Division UNICEF New York NY United States Department of International Health Johns Hopkins School of Public Health Baltimore MD United States School of Population Health University of Auckland Auckland New Zealand Department of Global Health and Development London School of Hygiene and Tropical Medicine London United Kingdom Health Systems Transformation Platform Tata Trusts Mumbai India Centre for Global Child Health Hospital for Sick Children Toronto OT Canada Global Financing Facility World Bank Washington DC United States Health Systems Research Unit South African Medical Research Council Cape Town South Africa Department of Health Management and Policy Beirut Lebanon Knowledge to Policy Center American University of Beirut Beirut Lebanon School of Public Health University of Western Cape Bellville South Africa Financing Alliance for Health Nairobi Kenya Institute for Global Health London United Kingdom Department of Economics London United Kingdom University College London London United Kingdom Health Policy and Systems Research Consultant Accra Ghana China Center for Health Development Studies Peking University Beijing China Program of Social Sciences and Health Latin American School of Social Sciences Buenos Aires Argentina Centre for Science and Environment New Delhi India Department of Maternal and Child Health Uganda Ministry of Health Kampala Uganda Institute of Child Health University of Ibadan Ibadan Nigeria Department of Health Systems WHO Phnom Penh Cambodia West and Central Regional Office UNICEF Dakar Senegal P

来源：评论

学校读者我要写书评

暂无评论

Perceptions of anonymised data use and awareness of the NHS data opt-out amongst patients, carers and healthcare staff

引用

research Involvement and Engagement 2021年第1期7卷 40页

作者： Atkin, C. Crosby, B. Dunn, K. Price, G. Marston, E. Crawford, C. O’Hara, M. Morgan, C. Levermore, M. Gallier, S. Modhwadia, S. Attwood, J. Perks, S. Denniston, A.K. Gkoutos, G. Dormer, R. Rosser, A. Ignatowicz, A. Fanning, H. Sapey, E. PIONEER Hub in Acute Care Institute of Inflammation and Ageing University Hospital Birmingham NHS Foundation Trust University of Birmingham Edgbaston Birmingham B15 2GW United Kingdom PIONEER HDR-UK Data Hub in Acute Care Institute of Inflammation and Ageing University Hospital Birmingham NHS Foundation Trust University of Birmingham Edgbaston Birmingham B15 2GW United Kingdom HDR-UK Midlands Physical Site University Hospital Birmingham NHS Foundation Trust University of Birmingham Edgbaston Birmingham B15 2GW United Kingdom Patient Involvement and Engagement Lead PIONEER London United Kingdom Research Support Services University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom Research and Development University Hospital Birmingham NHS Foundation Trust University of Birmingham Edgbaston Birmingham B15 2GW United Kingdom University Hospital Birmingham NHS Foundation Trust University of Birmingham Edgbaston Birmingham B15 2GW United Kingdom Public author Birmingham B15 2GW United Kingdom Medical Devices Technology International Limited (MDTi) The KaCe Building Victoria Passage Wolverhampton WV1 4LG West Midlands United Kingdom Health Education and Life Sciences Birmingham City University Birmingham West Midlands United Kingdom Technical Director PIONEER HDR-UK Data Hub in Acute Care Institute of Inflammation and Ageing University Hospital Birmingham NHS Foundation Trust University of Birmingham Edgbaston Birmingham B15 2GW United Kingdom PIONEER HDR-UK Data Hub in Acute Care University Hospital Birmingham NHS Foundation Trust University of Birmingham Edgbaston Birmingham B15 2GW United Kingdom Informatics University Hospital Birmingham NHS Foundation Trust University of Birmingham Edgbaston Birmingham B15 2GW United Kingdom Director of INSIGHT - the Health Data Research Hub for Eye Health University Hospitals Birmingham NHS Foundation Trust Edgbaston Birmingham B15 2GW United Kingdom Centre for Regulatory Science and Innovation

Background: England operates a National data Opt-Out (NDOO) for the secondary use of confidential health data for research and planning. We hypothesised that public awareness and support for the secondary use of health data and the NDOO would vary by participant demography and healthcare experience. We explored patient/public awareness and perceptions of secondary data use, grouping potential researchers into National Health Service (NHS), academia or commercial. We assessed awareness of the NDOO system amongst patients, carers, healthcare staff and the public. We co-developed recommendations to consider when sharing unconsented health data for research. Methods: A patient and public engagement program, co-created and including patient and public workshops, questionnaires and discussion groups regarding anonymised health data use. Results: There were 350 participants in total. Central concerns for health data use included unauthorised data re-use, the potential for discrimination and data sharing without patient benefit. 94% of respondents were happy for their data to be used for NHS research, 85% for academic research and 68% by health companies, but less than 50% for non-healthcare companies and opinions varied with demography and participant group. Questionnaires showed that knowledge of the NDOO was low, with 32% of all respondents, 53% of all NHS staff and 29% of all patients aware of the NDOO. Recommendations to guide unconsented secondary health data use included that health data use should benefit patients;data sharing decisions should involve patients/public. That data should remain in close proximity to health services with the principles of data minimisation applied. Further, that there should be transparency in secondary health data use, including publicly available lists of projects, summaries and benefits. Finally, organisations involved in data access decisions should participate in programmes to increase knowledge of the NDOO, to ensure public member

关键词： Anonymised healthcare data Commercial data sharing National data opt-out Secondary data use

来源：评论

学校读者我要写书评

暂无评论

OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

引用

Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

来源：评论

学校读者我要写书评

暂无评论

Correlation Analysis with Experimental Procedures and Peak Structures in Xps Spectral Round-Robin Tests on Mno Powder Sample

SSRN

引用

SSRN 2022年

作者： Murakami, Ryo Harada, Yoshitomo Sonobayashi, Yutaka Oji, Hiroshi Makino, Hisao Tanaka, Hiromi Taguchi, Hideyuki Sakamoto, Takanori Morita, Haruka Wakamori, Akihiko Kibe, Naoko Nishida, Shinsuke Nagata, Kenji Shinotsuka, Hiroshi Shouno, Hayaru Yoshikawa, Hideki The University of Electro-Communications Graduate School of Informatics and Engineering Chofu182-8585 Japan National Institute for Materials Science Research and Services Division of Materials Data and Integrated System Ibaraki Tsukuba305-0044 Japan Kyoto University Department of Materials Science and Engineering Kyoto606-8501 Japan Nagoya University Synchrotron radiation Research center Nagoya464-8603 Japan Kochi University of Technology School of Systems Engineering Japan National Institute of Technology Yonago College Department of Electrical and Computer Engineering Japan Nihon Parkerizing Co. Ltd Central Research Laboratories Kanagawa 254-0012 Japan JX Nippon Mining & Metals Corporation Technology Development Center Japan Melco Semiconductor Engineering Corporation Chemical Analysis & Evaluation Engineering Dept. Fukuoka Japan Kobelco Welding TechnoSolutions Co. Technical Solution Dept. Ltd Kanagawa 251-8551 Japan Furukawa Electric Co. Ltd Analysis Technology Center Sustainable Technology Laboratorie Kanagawa 220-0073 Japan

Spectral measurements provide valuable information on the electronic states and crystal structures of materials. In particular, X-ray photoelectron spectroscopy (XPS) can facilitate analysis of the chemical bond state of material surfaces. In the analysis of XPS spectra, the spectrum to be measured is typically compared to a reference spectrum measured on a known single-phase sample. Therefore, establishing a reference spectral database is imperative. In this study, round-robin tests of XPS spectra are performed with the aim of obtaining recommended XPS reference spectra and experimental procedures, including preprocessing. Determining the variations in peak structures between instrument- and sample-derived spectral shapes, and quantitatively discussing them in round-robin tests of XPS spectra are challenging. Therefore, an analysis method that automatically determines the above mentioned variations and extracts the samplederived common spectral structures is developed. This study presents a new approach to the quantitative analysis of round-robin tests of XPS spectra. The required common © 2022, The Authors. All rights reserved.

关键词： X ray photoelectron spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Biomedical data and AI

引用

Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

引用

LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

关键词：

来源：评论

学校读者我要写书评

暂无评论

Erratum: Interrupted mosaic speech revisited: Gain and loss in intelligibility by stretching [J. Acoust. Soc. Am. 155(3), 1767-1779 (2024)]

引用

The Journal of the Acoustical Society of America 2024年第5期155卷 3014页

作者： Kazuo Ueda Masashi Hashimoto Hiroshige Takeichi Kohei Wakamiya Department of Acoustic Design Faculty of Design/Research Center for Applied Perceptual Science/Research and Development Center for Five-Sense Devices Kyushu University 4-9-1 Shiobaru Minami-ku Fukuoka 815-8540 Japan. Department of Acoustic Design Faculty of Design Kyushu University 4-9-1 Shiobaru Minami-ku Fukuoka 815-8540 Japan. Open Systems Information Science Team Advanced Data Science Project (ADSP) RIKEN Information R&D and Strategy Headquarters (R-IH) RIKEN 1-7-22 Suehiro-cho Tsurumi-ku Yokohama Kanagawa 230-0045 Japan.

In the second line of the abstract on page 1767 of the original paper (Ueda , 2024), read “Udea” as “Ueda.”

关键词：

来源：评论

学校读者我要写书评

暂无评论

A phenome-wide association study of uterine fibroids reveals a marked burden of comorbidities

Communications Medicine

引用

Communications Medicine 2025年第1期5卷 174页

作者： Jasper, Elizabeth A. Mautz, Brian S. Hellwege, Jacklyn N. Piekos, Jacqueline A. Jones, Sarah H. Zhang, Yanfei Torstenson, Eric S. Pendergrass, Sarah A. Lee, Ming Ta Michael Edwards, Todd L. Velez Edwards, Digna R. Division of Quantitative and Clinical Sciences Department of Obstetrics and Gynecology Vanderbilt University Medical Center Nashville TN United States Center for Precision Medicine Vanderbilt University Medical Center Nashville TN United States Institute for Medicine and Public Health Vanderbilt University Medical Center Nashville TN United States Vanderbilt Epidemiology Center Vanderbilt University Nashville TN United States Vanderbilt Genetics Institute Vanderbilt University Nashville TN United States Population Analytics Analytics & Insights Data Sciences Janssen Research & Development Spring House PA United States Division of Genetic Medicine Department of Medicine Vanderbilt University Medical Center Nashville TN United States Genomic Medicine Institute Geisinger Health Systems Danville PA United States Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN United States Division of Epidemiology Department of Medicine Vanderbilt University Medical Center Nashville TN United States Genentech South San Francisco CA United States Department of Biomedical and Translational Informatics Geisinger Rockville MD United States

Background: The burden of comorbidities in those with uterine fibroids compared to those without fibroids is understudied. We performed a phenome-wide association study to systematically assess the association between fibroids and other conditions. Methods: Vanderbilt University Medical Center’s Synthetic Derivative and Geisinger Health System database, two electronic health record databases, were used for discovery and validation. Non-Hispanic Black and White females were included. Fibroid cases were identified through a previously validated algorithm. Race-stratified and multi-population phenome-wide association analyses, adjusting for age and body mass index, were performed before statistically significant, validated results were meta-analyzed. Results: There were 52,295 and 26,918 (9022 and 10,232 fibroid cases) females included in discovery and validation analyses. In multi-population meta-analysis, 389 conditions were associated with fibroid risk, with evidence of enrichment of circulatory, dermatologic, genitourinary, musculoskeletal, and sense organ conditions. The strongest associations within and across racial groups included conditions previously associated with fibroids. Numerous novel diagnoses, including cancers in female genital organs, were tied to fibroid status. Conclusions: Overall, individuals with fibroids have a marked increase in comorbidities compared to those without fibroids. This approach to evaluate the health context of fibroids highlights the potential to understand fibroid etiology through studying the common biology of comorbid diagnoses and through disease networks. © The Author(s) 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Inequality in infrastructure access and its association with health disparities

引用

Nature human behaviour 2025年 2025 May 22页

作者： Ying Tu Bin Chen Chuan Liao Shengbiao Wu Jiafu An Chen Lin Peng Gong Hong Wei Bing Xu Department of Earth System Science Ministry of Education Ecological Field Station for East Asian Migratory Birds Institute for Global Change Studies Tsinghua University Beijing China. Future Urbanity & Sustainable Environment (FUSE) Lab Division of Landscape Architecture Faculty of Architecture The University of Hong Kong Hong Kong SAR China. Department of Global Development Cornell University Ithaca NY USA. International Research Center of Big Data for Sustainable Development Goals Beijing China. School of Environment Beijing Normal University Beijing China. Department of Global Development Cornell University Ithaca NY USA. cl824@cornell.edu. Institute for Climate and Carbon Neutrality Urban Systems Institute The University of Hong Kong Hong Kong SAR China. Department of Real Estate and Construction Faculty of Architecture The University of Hong Kong Hong Kong SAR China. Faculty of Business and Economics The University of Hong Kong Hong Kong SAR China. Department of Geography and Department of Earth Sciences The University of Hong Kong Hong Kong SAR China. Department of Earth System Science Ministry of Education Ecological Field Station for East Asian Migratory Birds Institute for Global Change Studies Tsinghua University Beijing China. bingxu@***. International Research Center of Big Data for Sustainable Development Goals Beijing China. bingxu@***.

Economic, social and environmental infrastructure forms a fundamental pillar of societal development. Ensuring equitable access to infrastructure for all residents is crucial for achieving the Sustainable development Goals, yet knowledge gaps remain in infrastructure accessibility and inequality and their associations with human health. Here we generate gridded maps of economic, social and environmental infrastructure distribution and apply population-weighted exposure models and mixed-effects regressions to investigate differences in population access to infrastructure and their health implications across 166 countries. The results reveal contrasting inequalities in infrastructure access across regions and infrastructure types. Global South countries experience only 50-80% of the infrastructure access of Global North countries, whereas their associated inequality levels are 9-44% higher. Both infrastructure access and inequality are linked to health outcomes, with this relationship being especially pronounced in economic infrastructure. These findings underscore the necessity of informed decision-making to rectify infrastructure disparities for promoting human well-being.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

引用

arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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：