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arXiv 2021年

作者： Mondal, Suman Kumar Gorai, Prasanta Sil, Milan Ghosh, Rana Etim, Emmanuel E. Chakrabarti, Sandip K. Shimonishi, Takashi Nakatani, Naoki Furuya, Kenji Tan, Jonathan C. Das, Ankan Indian Centre for Space Physics 43 Chalantika Garia Station Road Kolkata700084 India Department of Space Earth and Environment Chalmers University of Technology GothenburgSE-412 96 Sweden Department of Chemical Sciences Federal University Wukari Katsina-Ala Road P.M.B. 1020 Taraba State Wukari Nigeria Center for Transdisciplinary Research Niigata University Nishi-ku Niigata950-2181 Japan Environmental Science Program Department of Science Faculty of Science Niigata University Nishi-ku Niigata950-2181 Japan Institute for Catalysis Hokkaido University N21W10 Kita-ku Hokkaido Sapporo001-0021 Japan Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan Department of Astronomy University of Virginia CharlottesvilleVA22904 United States Department of Chemistry Graduate School of Science and Engineering Tokyo Metropolitan University 1-1 Minami-Osawa Hachioji Tokyo192-0397 Japan National Astronomical Observatory of Japan Tokyo181-8588 Japan

It is speculated that there might be some linkage between interstellar aldehydes and their corresponding alcohols. Here, an observational study and astrochemical modeling are coupled together to illustrate the connection between them. The ALMA cycle 4 data of a hot molecular core, G10.47+0.03 is utilized for this study. Various aldehydes (acetaldehyde, propanal, and glycolaldehyde), alcohols (methanol and ethylene glycol), and a ketone (acetone) are identified in this source. The excitation temperatures and the column densities of these species were derived via the rotation diagram method assuming LTE conditions. An extensive investigation is carried out to understand the formation of these species. Six pairs of aldehyde-alcohol: i) methanal and methanol;ii) ethanal and ethanol;iii) propanal and 1-propanol;iv) propenal and allyl alcohol;v) propynal and propar gyl alcohol;vi) glycolaldehyde and ethylene glycol;vii) along with one pair of ketone-alcohol (acetone and isopropanol) and viii) ketene-alcohol (ethenone and vinyl alcohol) are considered for this study. Two succes sive hydrogenation reactions in the ice phase are examined to form these alcohols from aldehydes, ketone, and ketene, respectively. Quantum chemical methods are extensively executed to review the ice phase formation route and the kinetics of these species. Based on the obtained kinetic data, astrochemical modeling is employed to derive the abundances of these aldehydes, alcohols, ketone, and ketene in this source. It is seen that our model could successfully explain the observed abundances of various species in this hot molecular core. Copyright © 2021, The Authors. All rights reserved.

关键词： Acetone

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EMPRESS. IX. Extremely Metal-Poor Galaxies are Very Gas-Rich Dispersion-Dominated Systems: Will JWST Witness Gaseous Turbulent High-z Primordial Galaxies?

arXiv

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arXiv 2022年

作者： Isobe, Yuki Ouchi, Masami Nakajima, Kimihiko Ozaki, Shinobu Bouché, Nicolas F. Wise, John H. Xu, Yi Emsellem, Eric Kusakabe, Haruka Hattori, Takashi Nagao, Tohru Chiaki, Gen Fukushima, Hajime Harikane, Yuichi Hayashi, Kohei Hirai, Yutaka Kim, Ji Hoon Maseda, Michael V. Nagamine, Kentaro Shibuya, Takatoshi Sugahara, Yuma Yajima, Hidenobu Aoyama, Shohei Fujimoto, Seiji Fukushima, Keita Hatano, Shun Inoue, Akio K. Ishigaki, Tsuyoshi Kawasaki, Masahiro Kojima, Takashi Komiyama, Yutaka Koyama, Shuhei Koyama, Yusei Lee, Chien-Hsiu Matsumoto, Akinori Mawatari, Ken Moriya, Takashi J. Motohara, Kentaro Murai, Kai Nishigaki, Moka Onodera, Masato Ono, Yoshiaki Rauch, Michael Saito, Tomoki Sasaki, Rin Suzuki, Akihiro Takeuchi, Tsutomu T. Umeda, Hiroya Umemura, Masayuki Watanabe, Kuria Yabe, Kiyoto Zhang, Yechi Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan University of Tokyo Chiba Kashiwa277-8583 Japan Univ Lyon Univ Lyon1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-LavalF-69230 France Center for Relativistic Astrophysics School of Physics Georgia Institute of Technology AtlantaGA30332 United States Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan European Southern Observatory Karl-Schwarzschild-Straße 2 Garching85748 Germany Observatoire de Genéve Université de Genéve 51 Ch. des Maillettes Versoix1290 Switzerland 650 North A'ohoku Place HiloHI96720 United States Research Center for Space and Cosmic Evolution Ehime University Bunkyo-cho 2-5 Ehime Matsuyama790-8577 Japan Center for Computational Sciences University of Tsukuba Ten-nodai 1-1-1 Tsukuba Ibaraki305-8577 Japan Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom National Institute of Technology Ichinoseki College Hagisho Ichinoseki021-8511 Japan Astronomical Institute Tohoku University 6-3 Aoba Aramaki Aoba-ku Miyagi Sendai980-8578 Japan Department of Physics and Astronomy University of Notre Dame 225 Nieuwland Science Hall Notre DameIN46556 United States Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Center Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Astronomy University of Wisconsin-Madison 475 N. Charter Street MadisonWI53706 United States Theoretical Astrophysics Department of Earth & Space S

We present kinematics of 6 local extremely metal-poor galaxies (EMPGs) with low metallicities (0.016 − 0.098 Z☉) and low stellar masses (104.7 − 107.6M☉). Taking deep medium-high resolution (R ∼ 7500) integral-field spectra with 8.2-m Subaru, we resolve the small inner velocity gradients and dispersions of the EMPGs with Hα emission. Carefully masking out sub-structures originated by inflow and/or outflow, we fit 3-dimensional disk models to the observed Hα flux, velocity, and velocity-dispersion maps. All the EMPGs show rotational velocities (vrot) of 5-23 km s−1 smaller than the velocity dispersions (σ0) of 17-31 km s−1, indicating dispersion-dominated (vrot/σ0 = 0.29−0.80 gas 0.9 − 1.0. Comparing our results with other Hα kinematics studies, we find that vrot/σ0 decreases and fgas increases with decreasing metallicity, decreasing stellar mass, and increasing specific star-formation rate. We also find that simulated high-z (z ∼ 7) forming galaxies have gas fractions and dynamics similar to the observed EMPGs. Our EMPG observations and the simulations suggest that primordial galaxies are gas-rich dispersion-dominated systems, which would be identified by the forthcoming James Webb Space Telescope (JWST) observations at z ∼ 7. Copyright © 2022, The Authors. All rights reserved.

关键词： Galaxies

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Tequila: A platform for rapid development of quantum algorithms

arXiv

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arXiv 2020年

作者： Kottmann, Jakob S. Alperin-Lea, Sumner Tamayo-Mendoza, Teresa Cervera-Lierta, Alba Lavigne, Cyrille Yen, Tzu-Ching Verteletskyi, Vladyslav Schleich, Philipp Anand, Abhinav Degroote, Matthias Chaney, Skylar Kesibi, Maha Curnow, Naomi Grace Solo, Brandon Tsilimigkounakis, Georgios Zendejas-Morales, Claudia Izmaylov, Artur F. Aspuru-Guzik, Alán Chemical Physics Theory Group Department of Chemistry University of Toronto Canada Department of Computer Science University of Toronto Canada Department of Chemistry and Chemical Biology Harvard University United States Center for Computational Engineering Science RWTH Aachen University Aachen Germany Department of Physics Williams College WilliamstownMA United States University of Cambridge United Kingdom Quantum Open Source Foundation QC Mentorship Program Department of Physical and Environmental Sciences University of Toronto Scarborough Canada Vector Institute for Artificial Intelligence Toronto Canada Lebovic Fellow Toronto Canada

Variational quantum algorithms are currently the most promising class of algorithms for deployment on near-term quantum computers. In contrast to classical algorithms, there are almost no standardized methods in quantum algorithmic development yet, and the field continues to evolve rapidly. As in classical computing, heuristics play a crucial role in the development of new quantum algorithms, resulting in a high demand for flexible and reliable ways to implement, test, and share new ideas. Inspired by this demand, we introduce tequila, a development package for quantum algorithms in python, designed for fast and flexible implementation, prototyping and deployment of novel quantum algorithms in electronic structure and other fields. tequila operates with abstract expectation values which can be combined, transformed, differentiated, and optimized. On evaluation, the abstract data structures are compiled to run on state of the art quantum simulators or interfaces. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum theory

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Frequency-Resolved Optical Gating Recovery via Smoothing Gradient

arXiv

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arXiv 2019年

作者： Pinilla, Samuel Bendory, Tamir Eldar, Yonina C. Arguello, Henry Department of Electrical Engineering Universidad Industrial de Santander BucaramangaSantander680002 Colombia Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Weizmann Institute of Science Rehovot7610001 Israel Department of Computer Science Universidad Industrial de Santander BucaramangaSantander680002 Colombia

Frequency-resolved optical gating (FROG) is a popular technique for complete characterization of ultrashort laser pulses. The acquired data in FROG, called FROG trace, is the Fourier magnitude of the product of the unknown pulse with a time-shifted version of itself, for several different shifts. To estimate the pulse from the FROG trace, we propose an algorithm that minimizes a smoothed non-convex least-squares objective function. The method consists of two steps. First, we approximate the pulse by an iterative spectral algorithm. Then, the attained initialization is refined based upon a sequence of block stochastic gradient iterations. The algorithm is theoretically simple, numerically scalable, and easy-to-implement. Empirically, our approach outperforms the state-of-the-art when the FROG trace is incomplete, that is, when only few shifts are recorded. Simulations also suggest that the proposed algorithm exhibits similar computational cost compared to a state-of-the-art technique for both complete and incomplete data. In addition, we prove that in the vicinity of the true solution, the algorithm converges to a critical point. A Matlab implementation is publicly available at https://***/samuelpinilla/FROG. Copyright © 2019, The Authors. All rights reserved.

关键词： Ultrashort pulses

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Publisher Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

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Nature genetics 2023年第5期55卷 893页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Cambridge University Hospitals NHS Foundation Trust Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@***. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. hliang1@***. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson

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Correction: Profiling chromatin accessibility in pediatric acute lymphoblastic leukemia identifies subtype-specific chromatin landscapes and gene regulatory networks

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Leukemia 2024年第8期38卷 1867页

作者： Jonathan D Diedrich Qian Dong Daniel C Ferguson Brennan P Bergeron Robert J Autry Maoxiang Qian Wenjian Yang Colton Smith James B Papizan Jon P Connelly Kohei Hagiwara Kristine R Crews Shondra M Pruett-Miller Ching-Hon Pui Jun J Yang Mary V Relling William E Evans Daniel Savic Hematological Malignancies Program and Center for Precision Medicine in Leukemia St. Jude Children's Research Hospital Memphis TN USA. Department of Pharmaceutical Sciences St. Jude Children's Research Hospital Memphis TN USA. Graduate School of Biomedical Sciences St. Jude Children's Research Hospital Memphis TN USA. Integrated Biomedical Sciences Program University of Tennessee Health Science Center Memphis TN USA. Department of Cell and Molecular biology and Center for Advanced Genome Engineering St. Jude Children's Research Hospital Memphis TN USA. Department of Computational Biology St. Jude Children's Research Hospital Memphis TN USA. Department of Oncology St. Jude Children's Research Hospital Memphis TN USA. Department of Pathology St. Jude Children's Research Hospital Memphis TN USA. Hematological Malignancies Program and Center for Precision Medicine in Leukemia St. Jude Children's Research Hospital Memphis TN USA. daniel.savic@***. Department of Pharmaceutical Sciences St. Jude Children's Research Hospital Memphis TN USA. daniel.savic@***. Integrated Biomedical Sciences Program University of Tennessee Health Science Center Memphis TN USA. daniel.savic@***.

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Dynamic ¹³C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin (vol 23, 100855, 2020)

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Iscience 2020年第12期23卷 100855页

作者： Quek, Lake-Ee Krycer, James R. Ohno, Satoshi Yugi, Katsuyuki Fazakerley, Daniel J. Scalzo, Richard Elkington, Sarah D. Dai, Ziwei Hirayama, Akiyoshi Ikeda, Satsuki Shoji, Futaba Suzuki, Kumi Locasale, Jason W. Soga, Tomoyoshi James, David E. Kuroda, Shinya Charles Perkins Centre The University of Sydney Sydney NSW 2006 Australia School of Mathematics and Statistics The University of Sydney Sydney NSW 2006 Australia Sydney Medical School The University of Sydney Sydney NSW 2006 Australia Department of Biological Sciences Graduate School of Science University of Tokyo Hongo 7-3-1 Bunkyo-ku Tokyo 113-0033 Japan School of Life and Environmental Sciences The University of Sydney Sydney NSW 2006 Australia Institute for Advanced Biosciences Keio University Tsuruoka Yamagata 997-0052 Japan YCI Laboratory for Trans-Omics Young Chief Investigator Program RIKEN Center for Integrative Medical Sciences 1-7-22 Suehiro-cho Tsurumi-ku Yokohama Kanagawa 230-0045 Japan Faculty of Engineering and Information Technologies The University of Sydney Sydney NSW 2006 Australia Department of Pharmacology and Cancer Biology Duke University School of Medicine Duke University Durham NC 27710 USA AMED-CREST AMED 1-7-1 Otemachi Chiyoda-Ku Tokyo 100-0004 Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8562 Japan CREST Japan Science and Technology Agency Bunkyo-ku Tokyo 113-0033 Japan

Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13 C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.

关键词： Biological sciences Flux Data Metabolomics Metabolic Flux Analysis

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Visualizing structure and transitions in high-dimensional biological data (vol 54, pg 781, 2019)

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NATURE BIOTECHNOLOGY 2020年第1期38卷 108-108页

作者： Moon, Kevin R. van Dijk, David Wang, Zheng Gigante, Scott Burkhardt, Daniel B. Chen, William S. Yim, Kristina van den Elzen, Antonia Hirn, Matthew J. Coifman, Ronald R. Ivanova, Natalia B. Wolf, Guy Krishnaswamy, Smita Department of Mathematics and Statistics Utah State University Logan USA Cardiovascular Research Center section Cardiology Department of Internal Medicine Yale University New Haven USA Department of Computer Science Yale University New Haven USA School of Basic Medicine Qingdao University Qingdao China Yale Stem Cell Center Department of Genetics Yale University New Haven USA Computational Biology and Bioinformatics Program Yale University New Haven USA Department of Genetics Yale University New Haven USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing USA Department of Mathematics Michigan State University East Lansing USA Applied Mathematics Program Yale University New Haven USA Department of Genetics Center for Molecular Medicine University of Georgia Athens USA Department of Mathematics and Statistics Université de Montréal Montréal Canada Mila—Quebec Artificial Intelligence Institute Montréal Canada

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes (vol 12, pg 631, 2020)

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GENETICS IN MEDICINE 2020年第8期22卷 1427-1427页

作者： Chang, Willie H. Mashouri, Pouria Lozano, Alexander X. Johnstone, Brittney Husic, Mia Olry, Annie Maiella, Sylvie Balci, Tugce B. Sawyer, Sarah L. Robinson, Peter N. Rath, Ana Brudno, Michael Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada Department of Computer Science Princeton University Princeton NJ USA Faculty of Medicine University of Toronto Toronto ON Canada Department of Materials Science & Engineering Stanford University Stanford CA USA Sunnybrook Health Sciences Centre Toronto ON Canada Orphanet Institut national de la santé et de la recherche médicale Paris France Medical Genetics Program of Southwestern Ontario London Health Sciences Centre London ON Canada Department of Genetics Children’s Hospital of Eastern Ontario and Children’s Hospital of Eastern Ontario Research Institute University of Ottawa Ottawa ON Canada The Jackson Laboratory for Genomic Medicine Farmington CT USA Institute for Systems Genomics University of Connecticut Farmington CT USA Department of Computer Science University of Toronto Toronto ON Canada Genetics and Genome Biology Program The Hospital for Sick Children Toronto ON Canada University Health Network Toronto ON Canada

Purposecomputational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student *** developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated *** Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research *** enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

关键词： rare diseases phenotype crowdsourcing medical education machine learning

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