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Author Correction: Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer

Nature communications 2023年第1期14卷 5577页

作者： Un Jeong Kim Suyeon Lee Hyochul Kim Yeongeun Roh Seungju Han Hojung Kim Yeonsang Park Seokin Kim Myung Jin Chung Hyungbin Son Hyuck Choo Metaphotonics TU Samsung Advanced Institute of Technology Suwon Gyeonggi-do 16419 Republic of Korea. Machine Learning TU Samsung Advanced Institute of Technology Suwon Gyeonggi-do 16419 Republic of Korea. Department of Physics Chungnam National University Daejeon 34134 Korea. Institute of Quantum Systems Daejeon 34134 Korea. School of Integrative Engineering Chung-Ang University Seoul 06974 Republic of Korea. Department of Digital Health Samsung Advanced Institute of Health Science Sungkyunkwan University Seoul 06355 Korea. Department of Radiology Samsung Medical Center Sungkyunkwan University Seoul 06355 Korea. Department of Data Convergence and Future Medicine Sungkyunkwan University School of Medicine Suwon Gyeonggi-do 16419 Korea. Medical AI Research Center Research Institute for Future Medicine Samsung Medical Center Seoul 06351 Korea. Metaphotonics TU Samsung Advanced Institute of Technology Suwon Gyeonggi-do 16419 Republic of Korea. hyuck.choo@***.

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An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

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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

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Decoding the Molecular Universe - Workshop Report

arXiv

引用

arXiv 2023年

作者： Metz, Thomas O. Adkins, Joshua N. Armentrout, Peter B. Chain, Patrick Chu, Fanny Corley, Courtney D. Cort, John R. Denis, Elizabeth Drell, Daniel Duncan, Katherine R. Ewing, Robert G. Fernandez, Facundo M. Fiehn, Oliver Garg, Neha Grimme, Stefan Henry, Christopher Hettich, Robert L. Kind, Tobias Linington, Roger G. Miller, Gary W. Northen, Trent Overdahl, Kirsten Patrinos, Ari Raftery, Daniel Rigor, Paul Smith, Richard D. Sobus, Jon Teeguarden, Justin Vertes, Akos Waters, Katrina Webb-Robertson, Bobbie-Jo Williams, Antony Wishart, David Biological Sciences Division Earth and Biological Sciences Directorate Pacific Northwest National Laboratory RichlandWA United States Department of Chemistry University of Utah Salt Lake CityUT United States Biosciences Division Chemical Earth and Life Sciences Directorate Los Alamos National Laboratory Los AlamosNM United States Nuclear Chemistry and Biology Division National Security Directorate Pacific Northwest National Laboratory RichlandWA United States AI and Data Analytics Division Pacific Northwest National Laboratory RichlandWA United States Office of Biological and Environmental Research U.S. Department of Energy WashingtonDC United States University of Strathclyde Strathclyde Institute of Pharmacy and Biomedical Sciences Glasgow United Kingdom School of Chemistry and Biochemistry Petit Institute for Bioengineering and Bioscience Georgia Institute of Technology AtlantaGA United States University of California Davis West Coast Metabolomics Center DavisCA United States School of Chemistry and Biochemistry Petit Institute for Bioengineering and Bioscience Center for Microbial Dynamics and Infection Georgia Institute of Technology AtlantaGA United States Mulliken Center for Theoretical Chemistry Clausius Institute for Physical and Theoretical Chemistry University of Bonn Germany Data Science and Learning Division Argonne National Laboratory LemontIL United States Biosciences Division Oak Ridge National Laboratory Oak RidgeTN United States Enveda Biosciences BoulderCO United States Department of Chemistry Simon Fraser University BurnabyBC Canada Department of Environmental Health Sciences Mailman School of Public Health Columbia University New YorkNY United States Environmental Genomics and System Biology Division Lawrence Berkeley National Laboratory CA United States Metabolomics Core Facility National Institute of Environmental Health Sciences Research Triangle Park NC United States Novim Group Santa BarbaraCA United States Nor

On August 9-10, 2023, a workshop was convened at the Pacific Northwest National Laboratory (PNNL) in Richland, WA that brought together a group of internationally recognized experts in metabolomics, natural products discovery, chemical ecology, chemical and biological threat assessment, cheminformatics, computational chemistry, cloud computing, artificial intelligence, and novel technology development. These experts were invited to assess the value and feasibility of a grand-scale project to create new technologies that would allow the identification and quantification of all small molecules, or to "decode the molecular universe." The Decoding the Molecular Universe project would extend and complement the success of the Human Genome Project (HGP) by developing new capabilities and technologies to measure small molecules – defined as non-protein, non-polymer molecules less than ~1500 Daltons – of any origin and generated in biological systems or produced abiotically. The workshop was inspired by recognition that these new technologies would drive the next revolution in multiple fields of strategic importance to the nation: biological sciences, medical sciences, national security, chemistry, and the bio-economy. The workshop was supported by PNNL’s investment to advance the field of computational mass spectrometry in support of department of Energy and department of Homeland Security missions via a project named the m/q Initiative. The workshop was modeled on similar workshops and related efforts held in the mid-1980s that were the catalyst for the HGP. Workshop participants and observers included representatives from academia, government agencies, industry, and national laboratories whose research involves or where the success of programs and mission areas depends heavily on the study or measurement of such small molecules. Primary motivations for the workshop were a nearly universal need – across scientific disciplines and applied and basic research domains – to ide

关键词： Proteins

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Correction: Links between electroconvulsive therapy responsive and cognitive impairment multimodal brain networks in late-life major depressive disorder

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BMC medicine 2023年第1期21卷 113页

作者： Shile Qi Vince D Calhoun Daoqiang Zhang Jeremy Miller Zhi-De Deng Katherine L Narr Yvette Sheline Shawn M McClintock Rongtao Jiang Xiao Yang Joel Upston Tom Jones Jing Sui Christopher C Abbott College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China. shile.qi@***. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA. College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China. Department of Psychiatry University of New Mexico Albuquerque NM USA. Noninvasive Neuromodulation Unit Experimental Therapeutics & Pathophysiology Branch National Institute of Mental Health Bethesda MD USA. Departments of Neurology Psychiatry and Biobehavioral Sciences University of California Los Angeles CA USA. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. Division of Psychology Department of Psychiatry UT Southwestern Medical Center Dallas TX USA. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. Huaxi Brain Research Center West China Hospital of Sichuan University Chengdu China. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. jsui@***. Department of Psychiatry University of New Mexico Albuquerque NM USA. cabbott@salud.unm.edu.

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DESIGN AND IMPLEMENTATION OF THE ILLINOIS EXPRESS QUANTUM METROPOLITAN AREA NETWORK

arXiv

引用

arXiv 2022年

作者： Chung, Joaquin Eastman, Ely M. Kanter, Gregory S. Kapoor, Keshav Lauk, Nikolai Peña, Cristián Plunkett, Robert Sinclair, Neil Thomas, Jordan M. Valivarthi, Raju Xie, Si Kettimuthu, Rajkumar Kumar, Prem Spentzouris, Panagiotis Spiropulu, Maria Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States The University of Chicago Consortium for Advanced Science and Engineering 924 E 57th St ChicagoIL60637 United States NuCrypt Llc 1840 Oak Ave EvanstonIL60201 United States Center for Photonic Communication and Computing Department of Electrical and Computer Engineering McCormick School of Engineering and Applied Science Northwestern University 2145 Sheridan Road EvanstonIL60208-3118 United States Division of Physics Mathematics and Astronomy California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL60510 United States John A. Paulson School of Engineering and Applied Sciences Harvard University 29 Oxford St. CambridgeMA02138 United States 2205 Tech Drive Suite 1-160 EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208-3112 United States

The Illinois Express Quantum Network (IEQNET) is a program to realize metropolitan scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Q-Nodes) representing the communication parties in a quantum network. Q-Nodes generate or measure quantum signals such as entangled photons and communicate the measurement results via standard, classical signals and conventional networking processes. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via transparent optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages softwaredefined networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-Nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely implemented in the classical domain. We also discuss the IEQNET processes that address issues associated with synchronization, calibration, network monitoring, and scheduling. An important goal of IEQNET is to demonstrate the extent to which the control plane classical signals can co-propagate with the data plane quantum signals in the same fiber lines (quantum-classical signal "coexistence"). This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Star Cluster Classification using Deep Transfer learning with PHANGS-HST

arXiv

引用

arXiv 2023年

作者： Hannon, Stephen Whitmore, Bradley C. Lee, Janice C. Thilker, David A. Deger, Sinan Huerta, E.A. Wei, Wei Mobasher, Bahram Klessen, Ralf Boquien, Médéric Dale, Daniel A. Chevance, Mélanie Grasha, Kathryn Sanchez-Blazquez, Patricia Williams, Thomas Scheuermann, Fabian Groves, Brent Kim, Hwihyun Kruijssen, J.M. Diederik Department of Physics & Astronomy University of California RiversideCA92507 United States Space Telescope Science Institute BaltimoreMD21218 United States Gemini Observatory NSF’s NOIRLab 950 N. Cherry Avenue TucsonAZ85719 United States Steward Observatory University of Arizona TucsonAZ85721 United States Department of Physics and Astronomy The Johns Hopkins University BaltimoreMD21218 United States TAPIR California Institute of Technology PasadenaCA91125 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Zentrum für Astronomie der Universität Heidelberg Institut für Theoretische Astrophysik HeidelbergD-69120 Germany Universität Heidelberg Interdiszipliäres Zentrum für Wissenschaftliches Rechnen HeidelbergD-69120 Germany Instituto de Alta Investigación Universidad de Tarapacá Casilla 7D Arica Chile Department of Physics & Astronomy University of Wyoming LaramieWY82071 United States Research DAO coolresearch.io Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia Australia Departamento de Física de la Tierra y Astrofísica UCM Madrid28040 Spain Max Planck Institut für Astronomie Königstuhl17 Heidelberg69117 Germany Astronomisches Rechen-Institut Zentrum für Astronomie der Universität Heidelberg Mönchofstraße 12-14 HeidelbergD-69120 Germany International Centre for Radio Astronomy Research The University of Western Australia 7 Fairway CrawleyWA6009 Australia Research School of Astronomy and Astrophysics Australian National University Mt. Stromlo Observatory Weston CreekACT2611 Australia Technical University of Munich School of Engineering and Design Department of Aerospace and Geodesy Chair of Remote Sensing Technology Arcisstr. 21 Munich80333

Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models which accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D 20 Mpc). Here, we use HST UV-optical imaging of over 20,000 sources in 23 galaxies from the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: i) distance-dependent models, based on cluster candidates binned by galaxy distance (9–12 Mpc, 14–18 Mpc, 18–24 Mpc), and ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (∼60–80 per cent) and show improvement by a factor of two in classifying asymmetric and multi-peaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and SED-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N∼200,000) for public release. © 2023, CC BY.

关键词： Galaxies

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1364P Federated analysis of overall survival (OS) by location of metastases (mets) in patients (pts) with metastatic NSCLC (mNSCLC) from the Digital Oncology Network for Europe (DigiONE)

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Annals of Oncology 2024年 35卷 S859-S859页

作者： Å.K. Öjlert P. Jain L.E. Hendriks F.R. Ogliari F. Acker E. Bolton O. Bouissou J. Brash A-L. Bynens S. Cheeseman H.M. Fenton P. Galgane Banduge T. Goering A. Lobo Gomes R. McDonald E. Ross L. Sanchez Gomez S. Theophanous I. Zhovannik A. Helland Department of Oncology Oslo University Hospital Oslo Norway Non-Surgical Oncology Leeds Teaching Hospitals Trust Leeds UK Pulmonary Diseases Department Maastricht University Medical Center (MUMC) Maastricht Netherlands Dipartimento di Oncologia IRCCS Ospedale San Raffaele Milan Italy Department of Medicine II Hematology and Oncology Goethe University Frankfurt University Hospital Frankfurt am Main Germany Research and Innovation Centre Leeds Teaching Hospitals NHS Leeds UK Department for Technology and E-health Oslo University Hospital Oslo Norway OMOP & PPG Solutions IQVIA Ltd. London UK Clinical Data Science Maastricht University Medical Center Maastricht Netherlands Medical Oncology Leeds Teaching Hospitals Trust Leeds UK Oncology Evidence Network Leeds Teaching Hospital NHS Trust Leeds UK Department of Radiation Oncology (Maastro) Maastricht University Medical Center Maastricht Netherlands Kairos IQVIA Commercial GmbH & Co. OHG Bochum Germany Department of Radiation Oncology (Maastro) GROW Research Institute for Oncology and Reproduction Maastricht Netherlands Real-World Solutions IQVIA Ltd. London UK Federated Learning Medical Data Works B.V. Maastricht Netherlands Research and Innovation Leeds Teaching Hospitals NHS Trust Leeds UK Oncology Department University of Oslo Institute of Clinical medicine Oslo Norway

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Machine learning Techniques for Stress Prediction in Working Employees

Machine Learning Techniques for Stress Prediction in Working...

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IEEE International Conference on Computational Intelligence and Computing Research

作者： U Srinivasulu Reddy Aditya Vivek Thota A Dharun Department of Computer Applications Machine Learning and Data Analytics Lab National Institute of Technology Trichy ECE National Institute of Technology Trichy

ISBN: (纸本)9781538615096

Stress disorders are a common issue among working IT professionals in the industry today. With changing lifestyle and work cultures, there is an increase in the risk of stress among the employees. Though many industries and corporates provide mental health related schemes and try to ease the workplace atmosphere, the issue is far from control. In this paper, we would like to apply machine learning techniques to analyze stress patterns in working adults and to narrow down the factors that strongly determine the stress levels. Towards this, data from the OSMI mental health survey 2017 responses of working professionals within the tech-industry was considered. Various Machine learning techniques were applied to train our model after due data cleaning and preprocessing. The accuracy of the above models was obtained and studied comparatively. Boosting had the highest accuracy among the models implemented. By using Decision Trees, prominent features that influence stress were identified as gender, family history and availability of health benefits in the workplace. With these results, industries can now narrow down their approach to reduce stress and create a much comfortable workplace for their employees.

关键词： data handling decision trees DP management employee welfare ergonomics gender issues health care learning (artificial intelligence) occupational stress organisational aspects personnel psychology risk analysis computer installation management Decision Trees Organizational aspects gender issue data processing stress at work Ergonomics Psychology Risk Management risk analysis Personnel Employees Health Care data cleaning learning methods

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Target localization intervention and prognosis evaluation for an individual with mild cognitive impairment

Brain‐X

引用

Brain‐X 2023年第3期1卷

作者： Weiping Wang Haiyan Zhao Chang He Yuanbo Cui Zhen Wang Alexander Hramov Ping Luan Xiong Luo Jipeng Ouyang Kurths Jürgen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Writing - review & editing Department of Neurology Peking University Third Hospital Beijing China Contribution: Resources Zhen Wang The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi 710072 China. Contribution: Writing - original draft The 6th Research Institute of China Electronics Corporation Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Chang He School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China. Contribution: Investigation Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia Contribution: Data curation Guangdong Second Provincial General Hospital & Health Science Center Shenzhen Guangdong China Department of Neurology Shunde Hospital Southern Medical University Foshan Guangdong China Institute of Physics Humboldt-University Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany

Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

关键词： adaptive pinning control Alzheimer's disease BOLD signals computational modeling Sub_network

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Active learning Yields Better Training data for Scientific Named Entity Recognition

Active Learning Yields Better Training Data for Scientific N...

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IEEE International Conference on e-Science and Grid Computing

作者： Roselyne Tchoua Aswathy Ajith Zhi Hong Logan Ward Kyle Chard Debra Audus Shrayesh Patel Juan de Pablo Ian Foster University of Chicago Chicago IL USA Department of Computer Science University of Chicago Chicago IL USA Globus University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Argonne IL USA National Institute of Standards and Technology Gaithersburg MD USA

ISBN: (数字)9781728124513

ISBN: (纸本)9781728124520

Despite significant progress in natural language processing, machine learning models require substantial expertannotated training data to perform well in tasks such as named entity recognition (NER) and entity relations extraction. Furthermore, NER is often more complicated when working with scientific text. For example, in polymer science, chemical structure may be encoded using nonstandard naming conventions, the same concept can be expressed using many different terms (synonymy), and authors may refer to polymers with ad-hoc labels. These challenges, which are not unique to polymer science, make it difficult to generate training data, as specialized skills are needed to label text correctly. We have previously designed polyNER, a semi-automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based classifier. PolyNER facilitates a labeling process that is otherwise tedious and expensive. Here, we use active learning to efficiently obtain more annotations from experts and improve performance. Our approach requires just five hours of expert time to achieve discrimination capacity comparable to that of a state-of-the-art chemical NER toolkit.

关键词： Polymers Training data Labeling data mining Task analysis Chemicals Machine learning

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