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Lesmann H, Hustinx A, Moosa S, Klinkhammer H, Marchi E, Caro P, Abdelrazek IM, Pantel JT, Hagen MT, Thong MK, Binti Mazlan RA, Tae SK, Kamphans T, Meiswinkel W, Li JM, Javanmardi B, Knaus A, Uwineza A, Knopp C, Tkemaladze T, Elbracht M, Mattern L, Jamra RA, Velmans C, Strehlow V, Jacob M, Peron A, Dias C, Nunes BC, Vilella T, Pinheiro IF, Kim CA, Melaragno MI, Weiland H, Kaptain S, Chwiałkowska K, Kwasniewski M, Saad R, Wiethoff S, Goel H, Tang C, Hau A, Barakat TS, Panek P, Nabil A, Suh J, Braun F, Gomy I, Averdunk L, Ekure E, Bergant G, Peterlin B, Graziano C, Gaboon N, Fiesco-Roa M, Spinelli AM, Wilpert NM, Phowthongkum P, Güzel N, Haack TB, Bitar R, Tzschach A, Rodriguez-Palmero A, Brunet T, Rudnik-Schöneborn S, Contreras-Capetillo SN, Oberlack A, Samango-Sprouse C, Sadeghin T, Olaya M, Platzer K, Borovikov A, Schnabel F, Heuft L, Herrmann V, Oegema R, Elkhateeb N, Kumar S, Komlosi K, Mohamed K, Kalantari S, Sirchia F, Martinez-Monseny AF, Höller M, Toutouna L, Mohamed A, Lasa-Aranzasti A, Sayer JA, Ehmke N, Danyel M, Sczakiel H, Schwartzmann S, Boschann F, Zhao M, Adam R, Einicke L, Horn D, Chew KS, KAM CC, Karakoyun M, Pode-Shakked B, Eliyahu A, Rock R, Carrion T, Chorin O, Zarate YA, Conti MM, Karakaya M, Tung ML, Chandra B, Bouman A, Lumaka A, Wasif N, Shinawi M, Blackburn PR, Wang T, Niehues T, Schmidt A, Roth RR, Wieczorek D, Hu P, Waikel RL, Ledgister Hanchard SE, Elmakkawy G, Safwat S, Ebstein F, Krüger E, Küry S, Bézieau S, Arlt A, Olinger E, Marbach F, Li D, Dupuis L, Mendoza-Londono R, Houge SD, Weis D, Chung BHY, Mak CC, Kayserili H, Elcioglu N, Aykut A, Şimşek-Kiper PÖ, Bögershausen N, Wollnik B, Bentzen HB, Kurth I, Netzer C, Jezela-Stanek A, Devriendt K, Gripp KW, Mücke M, Verloes A, Schaaf CP, Nellåker C, Solomon BD, Nöthen MM, Abdalla E, Lyon GJ, Krawitz PM, Hsieh TC. GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases. RESEARCH SQUARE 2024:rs.3.rs-4438861. [PMID: 38903062 PMCID: PMC11188141 DOI: 10.21203/rs.3.rs-4438861/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.
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Affiliation(s)
- Hellen Lesmann
- Institute of Human Genetics, University of Bonn, Bonn, NRW, Germany
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
| | - Alexander Hustinx
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
| | - Shahida Moosa
- Division of Molecular Biology and Human Genetics, Stellenbosch University and Medical Genetics, Tygerberg Hospital, Stellenbosch, South Africa
| | - Hannah Klinkhammer
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
- Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, NRW, Germany
| | - Elaine Marchi
- New York State Institute for Basic Research in Developmental Disabilities, New York State, Albany, New York, USA
| | - Pilar Caro
- Institute of Human Genetics, Heidelberg University, Heidelberg, Baden-Württemberg, Germany
| | - Ibrahim M. Abdelrazek
- Department of Human Genetics, Medical Research Institute, Alexandria University, Alexandria, Alexandria, Egypt
| | - Jean Tori Pantel
- Institute for Digitalization and General Medicine, University Hospital RWTH Aachen, Aachen, NRW, Germany
- Centre for Rare Diseases Aachen (ZSEA), University Hospital RWTH Aachen, Aachen, NRW, Germany
| | - Merle ten Hagen
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
| | - Meow-Keong Thong
- Department of Paediatrics, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | | | - Sok Kun Tae
- Department of Paediatrics, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | | | | | - Jing-Mei Li
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
| | - Behnam Javanmardi
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
| | - Alexej Knaus
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
| | - Annette Uwineza
- College of Medicine and Health Sciences, University of Rwanda, and University Teaching Hospital of Kigali, Kigali, Rwanda
| | - Cordula Knopp
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, RWTH Aachen University, Aachen, NRW, Germany
| | - Tinatin Tkemaladze
- Department of Molecular and Medical Genetics, Tbilisi State Medical University, Tbilisi, Georgia
- Givi Zhvania Pediatric Academic Clinic, Tbilisi State Medical University, Georgia
| | - Miriam Elbracht
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, RWTH Aachen University, Aachen, NRW, Germany
| | - Larissa Mattern
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, RWTH Aachen University, Aachen, NRW, Germany
| | - Rami Abou Jamra
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Clara Velmans
- Institute of Human Genetics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, NRW, Germany
| | - Vincent Strehlow
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Maureen Jacob
- Institute of Human Genetics, Klinikum rechts der Isar, Technical University of Munich, School of Medicine and Health, Munich, Germany
| | - Angela Peron
- Medical Genetics, Meyer Children’s Hospital IRCCS, Firenze, Italy
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, Università degli Studi di Firenze, Italy
| | - Cristina Dias
- Department of Medical Genetics, Guy’s and St. Thomas’ NHS Foundation Trust, London, UK
- North East Thames Regional Genetics Service, Great Ormond Street Hospital for Children, Great Ormond Street, London, UK
- Neural Stem Cell Biology Laboratory, The Francis Crick Institute, UK
- Department of Medical & Molecular Genetics, School of Basic and Medical Biosciences, Faculty of Life Sciences & Medicine, King’s College London, UK
| | - Beatriz Carvalho Nunes
- Genetics Division, Department of Morphology and Genetics, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Thainá Vilella
- Genetics Division, Department of Morphology and Genetics, Universidade Federal de São Paulo, São Paulo, Brazil
| | | | - Chong Ae Kim
- Genetics Unit, Instituto da Criança, Universidade de São Paulo, São Paulo, Brazil
| | - Maria Isabel Melaragno
- Genetics Division, Department of Morphology and Genetics, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Hannah Weiland
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
| | - Sophia Kaptain
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
| | - Karolina Chwiałkowska
- Centre for Bioinformatics and Data Analysis, Medical University of Bialystok, Bialystok, Poland
- IMAGENE.ME SA, Bialystok, Poland
| | - Miroslaw Kwasniewski
- Centre for Bioinformatics and Data Analysis, Medical University of Bialystok, Bialystok, Poland
- IMAGENE.ME SA, Bialystok, Poland
| | - Ramy Saad
- North East Thames Regional Genetics Service, Great Ormond Street Hospital for Children, Great Ormond Street, London, UK
- Department of Genetics and Genomic Medicine, UCL Institute of Child Health, London UK
| | - Sarah Wiethoff
- Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, NRW, Germany
| | - Himanshu Goel
- School of Medicine and Public Health, University of Newcastle, Callaghan NSW, Australia
| | - Clara Tang
- Kabuki Syndrome Foundation, Northbrook, IL, USA
| | - Anna Hau
- Hunter Genetics, Hunter New England Health Service, Newcastle, Australia
| | - Tahsin Stefan Barakat
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Przemysław Panek
- Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, Warsaw, Poland
| | - Amira Nabil
- Department of Human Genetics, Medical Research Institute, Alexandria University, Alexandria, Alexandria, Egypt
| | - Julia Suh
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, RWTH Aachen University, Aachen, NRW, Germany
| | - Frederik Braun
- Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, NRW, Germany
| | - Israel Gomy
- Department of Genetics, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Sao Paulo, Brazil
| | - Luisa Averdunk
- Department of General Pediatrics and Neonatology, University Children’s Hospital, Heinrich-Heine-University, Medical Faculty, Düsseldorf, Germany
| | - Ekanem Ekure
- Department of Paediatrics, College of Medicine, University of Lagos, Lagos, Nigeria
| | - Gaber Bergant
- Clinical Institute of Genomic Medicine, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Borut Peterlin
- Clinical Institute of Genomic Medicine, University Medical Centre Ljubljana
| | | | - Nagwa Gaboon
- Medical Genetics Center, Faculty of Medicine, Ain Shams University, Cairo, Egypt
- Medical Genetics Department, Armed Forces College of Medicine, Cairo, Egypt
| | - Moisés Fiesco-Roa
- Programa de Maestría y Doctorado en Ciencias Médicas, Odontológicas y de la Salud, Universidad Nacional Autónoma de México, México City, Mexico
- Laboratorio de Citogenética, Instituto Nacional de Pediatría, México City, Mexico
| | | | - Nina-Maria Wilpert
- NeuroCure Cluster of Excellence; Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, D-10117 Berlin, Germany
- Department of Neuropediatrics, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, D-13353 Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Junior Clinician Scientist Program, D-10117 Berlin, German
| | - Prasit Phowthongkum
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
- Division of Medical Genetics and Genomics, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Nergis Güzel
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, RWTH Aachen University, Aachen, NRW, Germany
| | - Tobias B. Haack
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Rana Bitar
- Pediatric Gastroenterology Department, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates
- Khalifa University, Abu Dhabi, United Arab Emirates
| | - Andreas Tzschach
- Institute of Human Genetics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Agusti Rodriguez-Palmero
- Paediatric Neurology Unit, Department of Pediatrics, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Theresa Brunet
- Institute of Human Genetics, Klinikum rechts der Isar, Technical University of Munich, School of Medicine and Health, Munich, Germany
| | | | | | - Ava Oberlack
- Institute of Human Genetics, Klinikum rechts der Isar, Technical University of Munich, School of Medicine and Health, Munich, Germany
| | - Carole Samango-Sprouse
- Department of Pediatrics, George Washington University, 2121 I St. NW, Washington D.C. 2005
- Department of Human and Molecular Genetics, Florida International University, 11200 SW 8th Street, AHC2 Miami, Florida 22199
- Department of Research, The Focus Foundation, 820 W. Central Ave. #190, Davidsonville, MD 21035
| | - Teresa Sadeghin
- Department of Research, The Focus Foundation, 2772 Rutland Road P.O. Box 190, Davidsonville, MD 21035
| | - Margaret Olaya
- Department of Research, The Focus Foundation, 2772 Rutland Road P.O. Box 190, Davidsonville, MD 21035
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | | | - Franziska Schnabel
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Lara Heuft
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Vera Herrmann
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Renske Oegema
- Department of Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Nour Elkhateeb
- Department of Clinical Genetics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Sheetal Kumar
- Institute of Human Genetics, University of Bonn, Bonn, NRW, Germany
| | - Katalin Komlosi
- Institute of Human Genetics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Khoushoua Mohamed
- Department of Human Genetics, Medical Research Institute, Alexandria University, Alexandria, Alexandria, Egypt
| | - Silvia Kalantari
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Fabio Sirchia
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
- Medical Genetics Unit, IRCCS San Matteo Foundation, Pavia, Italy
| | - Antonio F. Martinez-Monseny
- Department of Clinical Genetics, SJD Barcelona Children’s Hospital, Esplugues del Llobregat (Barcelona), Spain
| | - Matthias Höller
- Institute of Human Genetics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Louiza Toutouna
- Institute of Human Genetics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Amal Mohamed
- Department of Human Genetics, Medical Research Institute, Alexandria University, Alexandria, Alexandria, Egypt
| | - Amaia Lasa-Aranzasti
- Medicine Genetics Group, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Barcelona Hospital Campus, Vall d’Hebron Hospital Universitari, Barcelona, Catalunya, Spain
- Department of Clinical and Molecular Genetics, Vall d’Hebron Barcelona Hospital Campus, Vall d’Hebron Hospital Universitari, Barcelona, Catalunya, Spain
| | - John A. Sayer
- Biosciences Institute, Newcastle University, Central Parkway, Newcastle upon Tyne, UK
- Renal Services, The Newcastle Upon Tyne NHS Hospitals Foundation Trust, Freeman Road, Newcastle Upon Tyne, UK
| | - Nadja Ehmke
- Institute of Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Magdalena Danyel
- Institute of Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Henrike Sczakiel
- Institute of Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Sarina Schwartzmann
- Institute of Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Felix Boschann
- Institute of Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Max Zhao
- Institute of Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Ronja Adam
- Institute of Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Lara Einicke
- Institute of Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Denise Horn
- Institute of Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Kee Seang Chew
- Department of Paediatrics, Faculty of Medicine, University Malaya, 59100 Kuala Lumpur, Malaysia
| | - Choy Chen KAM
- Department of Paediatrics, Faculty of Medicine, University Malaya, 59100 Kuala Lumpur, Malaysia
| | - Miray Karakoyun
- Ege University, Faculty of Medicine, Department of Pediatric Gastroenterology Hepatology and Nutrition, Izmir, Turkey
| | - Ben Pode-Shakked
- The Institute of Rare Diseases, Edmond and Lily Safra Children’s Hospital, Sheba Medical Center, Ramat Gan, Israel
- The faculty of Medical and Health Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Aviva Eliyahu
- The Danek Gertner Institute of Human Genetics, Sheba Medical Center, Tel-Hashomer, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Rachel Rock
- Metabolic Diseases Clinic, Edmond and Lily Safra Children’s Hospital, Sheba Medical Center
- National Newborn Screening Program, Public Health Services, Ministry of Health Tel-Hashomer, Israel
| | - Teresa Carrion
- Rare diseases Unit, Pediatric Department, Hospital Universitari Son Espases, Palma de Mallorca, Spain
| | - Odelia Chorin
- The Institute of Rare Diseases, Edmond and Lily Safra Children’s Hospital, Sheba Medical Center, Tel-Hashomer, Israel
| | - Yuri A. Zarate
- Department of Pediatrics, Section of Genetics and Metabolism, University of Arkansas for Medical Sciences and Arkansas Children’s Hospital, Little Rock, AR, USA
- Division of Genetics and Metabolism, University of Kentucky, Lexington, KY, USA
| | | | - Mert Karakaya
- Institute of Human Genetics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, NRW, Germany
| | - Moon Ley Tung
- University of Iowa Roy J and Lucille A Carver College of Medicine, Iowa City, IA 52242, USA
- Division of Medical Genetics and Genomics, Stead Family Department of Pediatrics, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA
| | - Bharatendu Chandra
- University of Iowa Roy J and Lucille A Carver College of Medicine, Iowa City, IA 52242, USA
- Division of Medical Genetics and Genomics, Stead Family Department of Pediatrics, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA
| | - Arjan Bouman
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Aime Lumaka
- Center for Human Genetics, Faculty of Medicine, University of Kinshasa, Kinshasa, DR Congo
| | - Naveed Wasif
- Institute of Human Genetics, University of Ulm, Ulm, Baden-Württemberg, Germany
- University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Marwan Shinawi
- Division of Genetics and Genomic Medicine, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Patrick R. Blackburn
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA
| | - Tianyun Wang
- Department of Medical Genetics, Center for Medical Genetics, Peking University Health Science Center, Beijing 100191, China
- Neuroscience Research Institute, Peking University; Key Laboratory for Neuroscience, Ministry of Education of China & National Health Commission of China, Beijing 100191, China
- Autism Research Center, Peking University Health Science Center, Beijing 100191, China
| | - Tim Niehues
- Department of Pediatrics, Helios Klinik Krefeld, Krefeld 47805, Germany
| | - Axel Schmidt
- Institute of Human Genetics, University of Bonn, Bonn, NRW, Germany
| | - Regina Rita Roth
- Institute of Human Genetics, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Dagmar Wieczorek
- Institute of Human Genetics, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Ping Hu
- Medical Genomics Unit, Medical Genetics Branch, National Human Genome Research Institute, Bethesda, USA
| | - Rebekah L. Waikel
- Medical Genomics Unit, Medical Genetics Branch, National Human Genome Research Institute, Bethesda, USA
| | | | - Gehad Elmakkawy
- Department of Human Genetics, Medical Research Institute, Alexandria University, Alexandria, Alexandria, Egypt
| | - Sylvia Safwat
- Department of Human Genetics, Medical Research Institute, Alexandria University, Alexandria, Alexandria, Egypt
| | - Frédéric Ebstein
- Nantes Université, CHU Nantes, CNRS, INSERM, l’institut du thorax, F-44000 Nantes, France
- Nantes Université, CHU Nantes, Service de Génétique Médicale, F-44000 Nantes, France
| | - Elke Krüger
- Insitute for Medical Biochemistry and Molecular Biology, University of Greifswald, Greifswald, Greifswald, Germany
| | - Sébastien Küry
- Nantes Université, CHU Nantes, CNRS, INSERM, l’institut du thorax, F-44000 Nantes, France
- Nantes Université, CHU Nantes, Service de Génétique Médicale, F-44000 Nantes, France
| | - Stéphane Bézieau
- Nantes Université, CHU Nantes, CNRS, INSERM, l’institut du thorax, F-44000 Nantes, France
- Nantes Université, CHU Nantes, Service de Génétique Médicale, F-44000 Nantes, France
| | - Annabelle Arlt
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
| | - Eric Olinger
- Center for Human Genetics, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Felix Marbach
- Institute of Human Genetics, Heidelberg University, Heidelberg, Baden-Württemberg, Germany
| | - Dong Li
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Lucie Dupuis
- Department to Paediatrics, Division of Clinical and Metabolic Genetics, The Hospital of Sick Children, Toronto, Ontario, Canada
| | - Roberto Mendoza-Londono
- Department to Paediatrics, Division of Clinical and Metabolic Genetics, The Hospital of Sick Children, Toronto, Ontario, Canada
| | - Sofia Douzgou Houge
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Denisa Weis
- Institue for Medical Genetics, Kepler University Hospital, Linz, Austria
| | - Brian Hon-Yin Chung
- Hong Kong Genome Institute, Hong Kong, China
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Christopher C.Y. Mak
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Hülya Kayserili
- Medical Genetics Department, Koç University School of Medicine (KUSoM), 34010, Istanbul, Türkiye
| | - Nursel Elcioglu
- Department of Pediatric Genetics, Marmara University School of Medicine, Istanbul, Türkiye
| | - Ayca Aykut
- Department of Medical Genetics, Ege University Faculty of Medicine, Izmir, Türkiye
| | | | - Nina Bögershausen
- Institut of Human Genetics, University Medical Center Göttingen, Göttingen, Germany
| | - Bernd Wollnik
- Institut of Human Genetics, University Medical Center Göttingen, Göttingen, Germany
- Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Göttingen, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Heidi Beate Bentzen
- Centre for Medical Ethics, Faculty of Medicine, University of Oslo, Oslo, Norway
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway
| | - Ingo Kurth
- Institute for Human Genetics and Genomic Medicine, Medical Faculty, RWTH Aachen University, Aachen, NRW, Germany
| | - Christian Netzer
- Institute of Human Genetics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, NRW, Germany
| | - Aleksandra Jezela-Stanek
- Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, Warsaw, Poland
| | | | - Karen W. Gripp
- Division of Medical Genetics, A.I. du Pont Hospital for Children/Nemours, USA, Wilmington, Delaware, USA
| | - Martin Mücke
- Institute for Digitalization and General Medicine, University Hospital RWTH Aachen, Aachen, NRW, Germany
- Centre for Rare Diseases Aachen (ZSEA), University Hospital RWTH Aachen, Aachen, NRW, Germany
| | - Alain Verloes
- Department of Clinical Genetics, Robert-Debré Hospital, Paris, France
| | - Christian P. Schaaf
- Institute of Human Genetics, Heidelberg University, Heidelberg, Baden-Württemberg, Germany
| | - Christoffer Nellåker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
| | - Benjamin D. Solomon
- Medical Genomics Unit, Medical Genetics Branch, National Human Genome Research Institute, Bethesda, USA
| | - Markus M. Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, NRW, Germany
| | - Ebtesam Abdalla
- Department of Human Genetics, Medical Research Institute, Alexandria University, Alexandria, Alexandria, Egypt
| | - Gholson J. Lyon
- Department of Human Genetics, New York State Institute for Basic Research in Developmental Disabilities, Staten Island, New York, United States of America
- George A. Jervis Clinic, New York State Institute for Basic Research in Developmental Disabilities, Staten Island, New York, United States of America
- Biology PhD Program, The Graduate Center, The City University of New York, New York, United States of America
| | - Peter M. Krawitz
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
| | - Tzung-Chien Hsieh
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, NRW, Germany
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McGuffie MJ, Barrick JE. Identifying widespread and recurrent variants of genetic parts to improve annotation of engineered DNA sequences. PLoS One 2024; 19:e0304164. [PMID: 38805426 PMCID: PMC11132462 DOI: 10.1371/journal.pone.0304164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/07/2024] [Indexed: 05/30/2024] Open
Abstract
Engineered plasmids have been workhorses of recombinant DNA technology for nearly half a century. Plasmids are used to clone DNA sequences encoding new genetic parts and to reprogram cells by combining these parts in new ways. Historically, many genetic parts on plasmids were copied and reused without routinely checking their DNA sequences. With the widespread use of high-throughput DNA sequencing technologies, we now know that plasmids often contain variants of common genetic parts that differ slightly from their canonical sequences. Because the exact provenance of a genetic part on a particular plasmid is usually unknown, it is difficult to determine whether these differences arose due to mutations during plasmid construction and propagation or due to intentional editing by researchers. In either case, it is important to understand how the sequence changes alter the properties of the genetic part. We analyzed the sequences of over 50,000 engineered plasmids using depositor metadata and a metric inspired by the natural language processing field. We detected 217 uncatalogued genetic part variants that were especially widespread or were likely the result of convergent evolution or engineering. Several of these uncatalogued variants are known mutants of plasmid origins of replication or antibiotic resistance genes that are missing from current annotation databases. However, most are uncharacterized, and 3/5 of the plasmids we analyzed contained at least one of the uncatalogued variants. Our results include a list of genetic parts to prioritize for refining engineered plasmid annotation pipelines, highlight widespread variants of parts that warrant further investigation to see whether they have altered characteristics, and suggest cases where unintentional evolution of plasmid parts may be affecting the reliability and reproducibility of science.
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Affiliation(s)
- Matthew J. McGuffie
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas, United States of America
| | - Jeffrey E. Barrick
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas, United States of America
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3
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McGuffie MJ, Barrick JE. Identifying widespread and recurrent variants of genetic parts to improve annotation of engineered DNA sequences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.10.536277. [PMID: 37090600 PMCID: PMC10120640 DOI: 10.1101/2023.04.10.536277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Engineered plasmids have been workhorses of recombinant DNA technology for nearly half a century. Plasmids are used to clone DNA sequences encoding new genetic parts and to reprogram cells by combining these parts in new ways. Historically, many genetic parts on plasmids were copied and reused without routinely checking their DNA sequences. With the widespread use of high-throughput DNA sequencing technologies, we now know that plasmids often contain variants of common genetic parts that differ slightly from their canonical sequences. Because the exact provenance of a genetic part on a particular plasmid is usually unknown, it is difficult to determine whether these differences arose due to mutations during plasmid construction and propagation or due to intentional editing by researchers. In either case, it is important to understand how the sequence changes alter the properties of the genetic part. We analyzed the sequences of over 50,000 engineered plasmids using depositor metadata and a metric inspired by the natural language processing field. We detected 217 uncatalogued genetic part variants that were especially widespread or were likely the result of convergent evolution or engineering. Several of these uncatalogued variants are known mutants of plasmid origins of replication or antibiotic resistance genes that are missing from current annotation databases. However, most are uncharacterized, and 3/5 of the plasmids we analyzed contained at least one of the uncatalogued variants. Our results include a list of genetic parts to prioritize for refining engineered plasmid annotation pipelines, highlight widespread variants of parts that warrant further investigation to see whether they have altered characteristics, and suggest cases where unintentional evolution of plasmid parts may be affecting the reliability and reproducibility of science.
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Affiliation(s)
- Matthew J. McGuffie
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas, United States
| | - Jeffrey E. Barrick
- Department of Molecular Biosciences, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas, United States
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4
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van Hoof M, Evans N, Inguaggiato G, Marušić A, Gordijn B, Dierickx K, van Zeggeren D, Dunnik H, Gesinn A, Bouter L, Widdershoven G. The Embassy of Good Science - a community driven initiative to promote ethics and integrity in research. OPEN RESEARCH EUROPE 2023; 2:27. [PMID: 37767226 PMCID: PMC10521075 DOI: 10.12688/openreseurope.14422.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/05/2023] [Indexed: 09/29/2023]
Abstract
The Embassy of Good Science ( https://www.embassy.science) aims to improve research integrity and research ethics by offering an online, open, 'go-to' platform, which brings together information on research integrity and research ethics and makes that information accessible, understandable, and appealing. It effectively organizes and describes research integrity and research ethics guidelines, educational materials, cases, and scenarios. The Embassy is wiki-based, allowing users to add -- when logged in with their ORCID researcher id -- new information, and update and refine existing information. The platform also makes the research integrity and research ethics community visible and more accessible in pages dedicated to relevant initiatives, news and events. Therefore, the Embassy enables researchers to find useful guidance, rules and tools to conduct research responsibly. The platform empowers researchers through increased knowledge and awareness, and through the support of the research integrity and research ethics community. In this article we will discuss the background of this new platform, the way in which it is organized, and how users can contribute.
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Affiliation(s)
- Marc van Hoof
- Department of Ethics, Law and Humanities, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
| | - Natalie Evans
- Department of Ethics, Law and Humanities, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
| | - Giulia Inguaggiato
- Department of Ethics, Law and Humanities, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
| | - Ana Marušić
- Department of Research in Biomedicine and Health, University of Split School of Medicine, Split, Split-Dalmatia, HR-21000, Croatia
| | - Bert Gordijn
- Institute of Ethics, Dublin City University, Dublin, Leinster, 9, Ireland
| | - Kris Dierickx
- Interfaculty Center for Biomedical Ethics and Law, KU Leuven, Leuven, 3000, Belgium
| | | | - Harald Dunnik
- Momkai BV, Amsterdam, Noord-Holland, 1013 NJ, The Netherlands
| | | | - Lex Bouter
- Department of Philosophy, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
| | - Guy Widdershoven
- Department of Ethics, Law and Humanities, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
| | - EnTIRE and VIRT2UE consortia
- Department of Ethics, Law and Humanities, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
- Department of Research in Biomedicine and Health, University of Split School of Medicine, Split, Split-Dalmatia, HR-21000, Croatia
- Institute of Ethics, Dublin City University, Dublin, Leinster, 9, Ireland
- Interfaculty Center for Biomedical Ethics and Law, KU Leuven, Leuven, 3000, Belgium
- Momkai BV, Amsterdam, Noord-Holland, 1013 NJ, The Netherlands
- Gesinn.it, Schwarzenfeld, 92521, Germany
- Department of Philosophy, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
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5
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Sosa DN, Altman RB. Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference. Brief Bioinform 2022; 23:6640007. [PMID: 35817308 PMCID: PMC9294417 DOI: 10.1093/bib/bbac268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/25/2022] [Accepted: 06/07/2022] [Indexed: 11/30/2022] Open
Abstract
The cost of drug development continues to rise and may be prohibitive in cases of unmet clinical need, particularly for rare diseases. Artificial intelligence-based methods are promising in their potential to discover new treatment options. The task of drug repurposing hypothesis generation is well-posed as a link prediction problem in a knowledge graph (KG) of interacting of drugs, proteins, genes and disease phenotypes. KGs derived from biomedical literature are semantically rich and up-to-date representations of scientific knowledge. Inference methods on scientific KGs can be confounded by unspecified contexts and contradictions. Extracting context enables incorporation of relevant pharmacokinetic and pharmacodynamic detail, such as tissue specificity of interactions. Contradictions in biomedical KGs may arise when contexts are omitted or due to contradicting research claims. In this review, we describe challenges to creating literature-scale representations of pharmacological knowledge and survey current approaches toward incorporating context and resolving contradictions.
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Affiliation(s)
- Daniel N Sosa
- Department of Biomedical Data Science, Stanford University, 443 Via Ortega, 94305, California, USA
| | - Russ B Altman
- Department of Biological Engineering; Department of Genetics; Department of Biomedical Data Science, Stanford University, 443 Via Ortega, 94305, California, USA
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6
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Levinson MA, Niestroy J, Al Manir S, Fairchild K, Lake DE, Moorman JR, Clark T. FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics. Neuroinformatics 2022; 20:187-202. [PMID: 34264488 PMCID: PMC8760356 DOI: 10.1007/s12021-021-09529-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/01/2021] [Indexed: 01/09/2023]
Abstract
Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result's metadata. An ontology for Evidence Graphs, EVI ( https://w3id.org/EVI ), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.
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Affiliation(s)
- Maxwell Adam Levinson
- Department of Public Health Sciences (Biomedical Informatics), University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Justin Niestroy
- Department of Public Health Sciences (Biomedical Informatics), University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Sadnan Al Manir
- Department of Public Health Sciences (Biomedical Informatics), University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Karen Fairchild
- Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA
- Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Douglas E Lake
- Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, VA, USA
- Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA
- Department of Statistics, University of Virginia College and Graduate School of Arts and Sciences, Charlottesville, VA, USA
| | - J Randall Moorman
- Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, VA, USA
- Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Timothy Clark
- Department of Public Health Sciences (Biomedical Informatics), University of Virginia School of Medicine, Charlottesville, VA, USA.
- Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, VA, USA.
- University of Virginia School of Data Science, Charlottesville, VA, USA.
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7
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Vogt L. FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example. J Biomed Semantics 2021; 12:20. [PMID: 34823588 PMCID: PMC8613519 DOI: 10.1186/s13326-021-00254-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/11/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND The size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). This can be achieved by using ontologies and through representing them as semantic graphs. Here, we discuss two different semantic graph approaches of representing empirical data and metadata in a knowledge graph, with phenotype descriptions as an example. Almost all phenotype descriptions are still being published as unstructured natural language texts, with far-reaching consequences for their FAIRness, substantially impeding their overall usability within the life sciences. However, with an increasing amount of anatomy ontologies becoming available and semantic applications emerging, a solution to this problem becomes available. Researchers are starting to document and communicate phenotype descriptions through the Web in the form of highly formalized and structured semantic graphs that use ontology terms and Uniform Resource Identifiers (URIs) to circumvent the problems connected with unstructured texts. RESULTS Using phenotype descriptions as an example, we compare and evaluate two basic representations of empirical data and their accompanying metadata in the form of semantic graphs: the class-based TBox semantic graph approach called Semantic Phenotype and the instance-based ABox semantic graph approach called Phenotype Knowledge Graph. Their main difference is that only the ABox approach allows for identifying every individual part and property mentioned in the description in a knowledge graph. This technical difference results in substantial practical consequences that significantly affect the overall usability of empirical data. The consequences affect findability, accessibility, and explorability of empirical data as well as their comparability, expandability, universal usability and reusability, and overall machine-actionability. Moreover, TBox semantic graphs often require querying under entailment regimes, which is computationally more complex. CONCLUSIONS We conclude that, from a conceptual point of view, the advantages of the instance-based ABox semantic graph approach outweigh its shortcomings and outweigh the advantages of the class-based TBox semantic graph approach. Therefore, we recommend the instance-based ABox approach as a FAIR approach for documenting and communicating empirical data and metadata in a knowledge graph.
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Affiliation(s)
- Lars Vogt
- TIB Leibniz Information Centre for Science and Technology, Welfengarten 1B, 30167, Hanover, Germany.
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8
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Wang X, Song N, Zhou H, Cheng H. The representation of argumentation in scientific papers: A comparative analysis of two research areas. J Assoc Inf Sci Technol 2021. [DOI: 10.1002/asi.24590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Xiaoguang Wang
- School of Information Management Wuhan University Wuhan China
| | - Ningyuan Song
- School of Information Management Nanjing University Nanjing China
| | - Huimin Zhou
- School of Information Management Wuhan University Wuhan China
| | - Hanghang Cheng
- School of Information Management Wuhan University Wuhan China
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9
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Leipzig J, Nüst D, Hoyt CT, Ram K, Greenberg J. The role of metadata in reproducible computational research. PATTERNS (NEW YORK, N.Y.) 2021; 2:100322. [PMID: 34553169 PMCID: PMC8441584 DOI: 10.1016/j.patter.2021.100322] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.
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Affiliation(s)
- Jeremy Leipzig
- Metadata Research Center, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA
| | - Daniel Nüst
- Institute for Geoinformatics, University of Münster, Münster, Germany
| | | | - Karthik Ram
- Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA
| | - Jane Greenberg
- Metadata Research Center, College of Computing and Informatics, Drexel University, Philadelphia, PA, USA
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10
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Hobbs ET, Goralski SM, Mitchell A, Simpson A, Leka D, Kotey E, Sekira M, Munro JB, Nadendla S, Jackson R, Gonzalez-Aguirre A, Krallinger M, Giglio M, Erill I. ECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts. Front Res Metr Anal 2021; 6:674205. [PMID: 34327299 PMCID: PMC8313968 DOI: 10.3389/frma.2021.674205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 06/28/2021] [Indexed: 11/20/2022] Open
Abstract
Analysis of high-throughput experiments in the life sciences frequently relies upon standardized information about genes, gene products, and other biological entities. To provide this information, expert curators are increasingly relying on text mining tools to identify, extract and harmonize statements from biomedical journal articles that discuss findings of interest. For determining reliability of the statements, curators need the evidence used by the authors to support their assertions. It is important to annotate the evidence directly used by authors to qualify their findings rather than simply annotating mentions of experimental methods without the context of what findings they support. Text mining tools require tuning and adaptation to achieve accurate performance. Many annotated corpora exist to enable developing and tuning text mining tools; however, none currently provides annotations of evidence based on the extensive and widely used Evidence and Conclusion Ontology. We present the ECO-CollecTF corpus, a novel, freely available, biomedical corpus of 84 documents that captures high-quality, evidence-based statements annotated with the Evidence and Conclusion Ontology.
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Affiliation(s)
- Elizabeth T Hobbs
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Stephen M Goralski
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Ashley Mitchell
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Andrew Simpson
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Dorjan Leka
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Emmanuel Kotey
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Matt Sekira
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
| | - James B Munro
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Suvarna Nadendla
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Rebecca Jackson
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
| | | | - Martin Krallinger
- Barcelona Supercomputing Center (BSC), Barcelona, Spain.,Centro Nacional de Investigaciones Oncológicas (CNIO), Madrid, Spain
| | - Michelle Giglio
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Ivan Erill
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States
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11
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Luo M, Cohen AM, Addepalli S, Smalheiser NR. Identifying main finding sentences in clinical case reports. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2020:5855433. [PMID: 32525207 PMCID: PMC7287507 DOI: 10.1093/database/baaa041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/10/2020] [Accepted: 05/06/2020] [Indexed: 11/20/2022]
Abstract
Clinical case reports are the ‘eyewitness reports’ of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally, a case report has a single main finding that represents the reason for writing up the report in the first place. However, no one has previously created an automatic way of identifying main finding sentences in case reports. We previously created a manual corpus of main finding sentences extracted from the abstracts and full text of clinical case reports. Here, we have utilized the corpus to create a machine learning-based model that automatically predicts which sentence(s) from abstracts state the main finding. The model has been evaluated on a separate manual corpus of clinical case reports and found to have good performance. This is a step toward setting up a retrieval system in which, given one case report, one can find other case reports that report the same or very similar main findings. The code and necessary files to run the main finding model can be downloaded from https://github.com/qi29/main_ finding_recognition, released under the Apache License, Version 2.0.
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Affiliation(s)
- Mengqi Luo
- Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL 60612, USA.,School of Information Management, Wuhan University, Wuhan, Hubei 430072, China
| | - Aaron M Cohen
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Sidharth Addepalli
- Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL 60612, USA
| | - Neil R Smalheiser
- Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL 60612, USA
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12
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Leung TI, Pendharkar S, Chen CYA, Dumontier M. Finding the Evidence Base Using Citation Networks: Do 300 to 400 US Physicians Die by Suicide Annually? J Gen Intern Med 2021; 36:1129-1131. [PMID: 32462565 PMCID: PMC8041966 DOI: 10.1007/s11606-020-05824-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 03/26/2020] [Indexed: 11/30/2022]
Affiliation(s)
- Tiffany I Leung
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Postbus 5800, 6202 AZ, Maastricht, The Netherlands.
| | - Sima Pendharkar
- Division of Hospital Medicine, The Brooklyn Hospital Center, Icahn School of Medicine Mt. Sinai, Brooklyn, NY, 11206, USA
| | - Chwen-Yuen Angie Chen
- Department of Primary Care and Population Health, Stanford University, 211 Quarry Road, 4th Floor, Hoover Pavilion, Palo Alto, CA, 94304, USA
| | - Michel Dumontier
- Institute of Data Science, Maastricht University, Paul-Henri Spaaklaan 1, 6229 GT, Maastricht, The Netherlands
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13
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Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context. Scientometrics 2021; 126:6225-6251. [PMID: 33612884 PMCID: PMC7882417 DOI: 10.1007/s11192-021-03880-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 01/19/2021] [Indexed: 11/05/2022]
Abstract
In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept “knowledge unit” and “knowmetrics” for measuring knowledge. However, the definition on “computable knowledge object” remains controversial so far in different fields. For example, it is defined as (1) quantitative scientific concept in natural science and engineering, (2) knowledge point in the field of education research, and (3) semantic predications, i.e., Subject-Predicate-Object (SPO) triples in biomedical fields. The Semantic MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples extracted from medical literature, provides a basic data infrastructure for measuring medical knowledge. In general, the study of extracting SPO triples as computable knowledge unit from unstructured scientific text has been overwhelmingly focusing on scientific knowledge per se. Since the SPO triples would be possibly extracted from hypothetical, speculative statements or even conflicting and contradictory assertions, the knowledge status (i.e., the uncertainty), which serves as an integral and critical part of scientific knowledge has been largely overlooked. This article aims to put forward a framework for Medical Knowmetrics using the SPO triples as the knowledge unit and the uncertainty as the knowledge context. The lung cancer publications dataset is used to validate the proposed framework. The uncertainty of medical knowledge and how its status evolves over time indirectly reflect the strength of competing knowledge claims, and the probability of certainty for a given SPO triple. We try to discuss the new insights using the uncertainty-centric approaches to detect research fronts, and identify knowledge claims with high certainty level, in order to improve the efficacy of knowledge-driven decision support.
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14
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Cui H, Zhang L, Ford B, Cheng HL, Macklin JA, Reznicek A, Starr J. Measurement Recorder: developing a useful tool for making species descriptions that produces computable phenotypes. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5995854. [PMID: 33216896 PMCID: PMC7678789 DOI: 10.1093/database/baaa079] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/24/2020] [Accepted: 08/27/2020] [Indexed: 12/31/2022]
Abstract
To use published phenotype information in computational analyses, there have been efforts to convert descriptions of phenotype characters from human languages to ontologized statements. This postpublication curation process is not only slow and costly, it is also burdened with significant intercurator variation (including curator-author variation), due to different interpretations of a character by various individuals. This problem is inherent in any human-based intellectual activity. To address this problem, making scientific publications semantically clear (i.e. computable) by the authors at the time of publication is a critical step if we are to avoid postpublication curation. To help authors efficiently produce species phenotypes while producing computable data, we are experimenting with an author-driven ontology development approach and developing and evaluating a series of ontology-aware software modules that would create publishable species descriptions that are readily useable in scientific computations. The first software module prototype called Measurement Recorder has been developed to assist authors in defining continuous measurements and reported in this paper. Two usability studies of the software were conducted with 22 undergraduate students majoring in information science and 32 in biology. Results suggest that participants can use Measurement Recorder without training and they find it easy to use after limited practice. Participants also appreciate the semantic enhancement features. Measurement Recorder's character reuse features facilitate character convergence among participants by 48% and have the potential to further reduce user errors in defining characters. A set of software design issues have also been identified and then corrected. Measurement Recorder enables authors to record measurements in a semantically clear manner and enriches phenotype ontology along the way. Future work includes representing the semantic data as Resource Description Framework (RDF) knowledge graphs and characterizing the division of work between authors as domain knowledge providers and ontology engineers as knowledge formalizers in this new author-driven ontology development approach.
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Affiliation(s)
- Hong Cui
- School of Information, University of Arizona, Tucson, AZ 85705, USA
| | - Limin Zhang
- School of Information, University of Arizona, Tucson, AZ 85705, USA
| | - Bruce Ford
- Department of Biological sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Hsin-Liang Cheng
- Curtis Laws Wilson Library, Missouri University of Science and Technology, Rolla, MO 65409, USA
| | - James A Macklin
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
| | - Anton Reznicek
- LSA Herbarium, University of Michigan, Ann Arbor, MI 48019, USA
| | - Julian Starr
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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15
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Fu Y, Schneider J. Towards Knowledge Maintenance in Scientific Digital Libraries with the Keystone Framework. PROCEEDINGS OF THE ... ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES. ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES 2020; 2020:217-226. [PMID: 34305485 PMCID: PMC8300994 DOI: 10.1145/3383583.3398514] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Scientific digital libraries speed dissemination of scientific publications, but also the propagation of invalid or unreliable knowledge. Although many papers with known validity problems are highly cited, no auditing process is currently available to determine whether a citing paper's findings fundamentally depend on invalid or unreliable knowledge. To address this, we introduce a new framework, the keystone framework, designed to identify when and how citing unreliable findings impacts a paper, using argumentation theory and citation context analysis. Through two pilot case studies, we demonstrate how the keystone framework can be applied to knowledge maintenance tasks for digital libraries, including addressing citations of a non-reproducible paper and identifying statements most needing validation in a high-impact paper. We identify roles for librarians, database maintainers, knowledgebase curators, and research software engineers in applying the framework to scientific digital libraries.
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Affiliation(s)
- Yuanxi Fu
- School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL USA
| | - Jodi Schneider
- School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL USA
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16
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Lock A, Harris MA, Rutherford K, Hayles J, Wood V. Community curation in PomBase: enabling fission yeast experts to provide detailed, standardized, sharable annotation from research publications. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5827230. [PMID: 32353878 PMCID: PMC7192550 DOI: 10.1093/database/baaa028] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 02/28/2020] [Accepted: 03/22/2020] [Indexed: 11/22/2022]
Abstract
Maximizing the impact and value of scientific research requires efficient knowledge distribution, which increasingly depends on the integration of standardized published data into online databases. To make data integration more comprehensive and efficient for fission yeast research, PomBase has pioneered a community curation effort that engages publication authors directly in FAIR-sharing of data representing detailed biological knowledge from hypothesis-driven experiments. Canto, an intuitive online curation tool that enables biologists to describe their detailed functional data using shared ontologies, forms the core of PomBase’s system. With 8 years’ experience, and as the author response rate reaches 50%, we review community curation progress and the insights we have gained from the project. We highlight incentives and nudges we deploy to maximize participation, and summarize project outcomes, which include increased knowledge integration and dissemination as well as the unanticipated added value arising from co-curation by publication authors and professional curators.
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Affiliation(s)
- Antonia Lock
- Department of Genetics, Evolution and Environment, University College London, Gower street, London WC1E 6BT, UK
| | - Midori A Harris
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Kim Rutherford
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Jacqueline Hayles
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK
| | - Valerie Wood
- Cell Cycle Laboratory, The Francis Crick Institute, Midland Rd, London NW1 1AT, UK
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17
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Prieto M, Deus H, de Waard A, Schultes E, García-Jiménez B, Wilkinson MD. Data-driven classification of the certainty of scholarly assertions. PeerJ 2020; 8:e8871. [PMID: 32341891 PMCID: PMC7182025 DOI: 10.7717/peerj.8871] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 03/09/2020] [Indexed: 01/02/2023] Open
Abstract
The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty; however, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three distinct categories of certainty along a spectrum from high to low. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation-a Nanopublication-where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.
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Affiliation(s)
- Mario Prieto
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Pozuelo de Alarcon, Madrid, Spain
| | - Helena Deus
- Elsevier Inc., Cambridge, MA, United States of America
| | - Anita de Waard
- Elsevier Research Collaborations Unit, Jericho, VT, United States of America
| | - Erik Schultes
- GO FAIR International Support and Coordination Office, Leiden, The Netherlands
| | - Beatriz García-Jiménez
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Pozuelo de Alarcon, Madrid, Spain
| | - Mark D. Wilkinson
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Pozuelo de Alarcon, Madrid, Spain
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Abstract
Preprints are gaining visibility in many fields. Thanks to the exponential growth in submissions to bioRxiv, an online server for preprints in biology, versions of manuscripts prior to the completion of journal-organized peer review are poised to become a standard component of the publishing experience in the life sciences. Here, we provide an overview of current challenges facing preprints, both technical and social, and a vision for their future development.
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Herman E, Akeroyd J, Bequet G, Nicholas D, Watkinson A. The changed – and changing – landscape of serials publishing: Review of the literature on emerging models. LEARNED PUBLISHING 2020. [DOI: 10.1002/leap.1288] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Eti Herman
- CIBER Research Ltd Newbury, Berkshire RG147RU UK
| | - John Akeroyd
- CIBER Research Ltd Newbury, Berkshire RG147RU UK
| | - Gaelle Bequet
- ISSN International Centre/CIEPS/Centro internacional del ISSN 45 Rue de Turbigo, 75003 Paris France
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Chehab K, Kalboussi A, Hadj Kacem A. Study of Healthcare Professionals’ Interaction in the Patient Records Based on Annotations. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7313271 DOI: 10.1007/978-3-030-51517-1_28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The annotation practice is an almost daily activity; it is used by healthcare professionals (PHC) to analyze, collaborate, share knowledge and communicate, between them, information present in the healthcare record of patients. These annotations are created in a healthcare cycle that consists of: diagnosis, treatment, advice, follow-up and observation.
Due to an exponential increase in the number of medical annotation systems that are used by different categories of health professionals, we are faced with a problem of lack of organization of medical annotation systems developed on the basis of formal criteria. As a result, we have a fragmented image of these annotations tools which make the mission of choice of an annotation system by a PHC, in a well-defined context (biology, radiology…) and according to their needs to the functionalities offered by these tools, are difficult.
In this article we present a classification of thirty annotation tools developed by industry and academia based on 5 generic criteria. We conclude this survey paper with model proposition.
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Khan FZ, Soiland-Reyes S, Sinnott RO, Lonie A, Goble C, Crusoe MR. Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv. Gigascience 2019; 8:giz095. [PMID: 31675414 PMCID: PMC6824458 DOI: 10.1093/gigascience/giz095] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 05/23/2019] [Accepted: 07/17/2019] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND The automation of data analysis in the form of scientific workflows has become a widely adopted practice in many fields of research. Computationally driven data-intensive experiments using workflows enable automation, scaling, adaptation, and provenance support. However, there are still several challenges associated with the effective sharing, publication, and reproducibility of such workflows due to the incomplete capture of provenance and lack of interoperability between different technical (software) platforms. RESULTS Based on best-practice recommendations identified from the literature on workflow design, sharing, and publishing, we define a hierarchical provenance framework to achieve uniformity in provenance and support comprehensive and fully re-executable workflows equipped with domain-specific information. To realize this framework, we present CWLProv, a standard-based format to represent any workflow-based computational analysis to produce workflow output artefacts that satisfy the various levels of provenance. We use open source community-driven standards, interoperable workflow definitions in Common Workflow Language (CWL), structured provenance representation using the W3C PROV model, and resource aggregation and sharing as workflow-centric research objects generated along with the final outputs of a given workflow enactment. We demonstrate the utility of this approach through a practical implementation of CWLProv and evaluation using real-life genomic workflows developed by independent groups. CONCLUSIONS The underlying principles of the standards utilized by CWLProv enable semantically rich and executable research objects that capture computational workflows with retrospective provenance such that any platform supporting CWL will be able to understand the analysis, reuse the methods for partial reruns, or reproduce the analysis to validate the published findings.
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Affiliation(s)
- Farah Zaib Khan
- The University of Melbourne, School of Computing and Information System, Doug Mcdonnell Building, Parkville, Australia, 3052
- Common Workflow Language Project
| | | | - Richard O Sinnott
- The University of Melbourne, School of Computing and Information System, Doug Mcdonnell Building, Parkville, Australia, 3052
| | - Andrew Lonie
- The University of Melbourne, School of Computing and Information System, Doug Mcdonnell Building, Parkville, Australia, 3052
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Rosemblat G, Fiszman M, Shin D, Kilicoglu H. Towards a characterization of apparent contradictions in the biomedical literature using context analysis. J Biomed Inform 2019; 98:103275. [PMID: 31473364 DOI: 10.1016/j.jbi.2019.103275] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 08/26/2019] [Accepted: 08/28/2019] [Indexed: 11/19/2022]
Abstract
BACKGROUND With the substantial growth in the biomedical research literature, a larger number of claims are published daily, some of which seemingly disagree with or contradict prior claims on the same topics. Resolving such contradictions is critical to advancing our understanding of human disease and developing effective treatments. Automated text analysis techniques can facilitate such analysis by extracting claims from the literature, flagging those that are potentially contradictory, and identifying any study characteristics that may explain such contradictions. METHODS Using SemMedDB, our own PubMed-scale repository of semantic predications (subject-relation-object triples), we identified apparent contradictions in the biomedical research literature and developed a categorization of contextual characteristics that explain such contradictions. Clinically relevant semantic predications relating to 20 diseases and involving opposing predicate pairs (e.g., an intervention treats or causes a disease) were retrieved from SemMedDB. After addressing inference, uncertainty, generic concepts, and NLP errors through automatic and manual filtering steps, a set of apparent contradictions were identified and characterized. RESULTS We retrieved 117,676 predication instances from 62,360 PubMed abstracts (Jan 1980-Dec 2016). From these instances, automatic filtering steps generated 2236 candidate contradictory pairs. Through manual analysis, we determined that 58 of these pairs (2.6%) were apparent contradictions. We identified five main categories of contextual characteristics that explain these contradictions: (a) internal to the patient, (b) external to the patient, (c) endogenous/exogenous, (d) known controversy, and (e) contradictions in literature. Categories (a) and (b) were subcategorized further (e.g., species, dosage) and accounted for the bulk of the contradictory information. CONCLUSIONS Semantic predications, by accounting for lexical variability, and SemMedDB, owing to its literature scale, can support identification and elucidation of potentially contradictory claims across the biomedical domain. Further filtering and classification steps are needed to distinguish among them the true contradictory claims. The ability to detect contradictions automatically can facilitate important biomedical knowledge management tasks, such as tracking and verifying scientific claims, summarizing research on a given topic, identifying knowledge gaps, and assessing evidence for systematic reviews, with potential benefits to the scientific community. Future work will focus on automating these steps for fully automatic recognition of contradictions from the biomedical research literature.
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Affiliation(s)
- Graciela Rosemblat
- National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.
| | - Marcelo Fiszman
- National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.
| | - Dongwook Shin
- National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.
| | - Halil Kilicoglu
- National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.
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23
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Verified, Shared, Modular, and Provenance Based Research Communication with the Dat Protocol. PUBLICATIONS 2019. [DOI: 10.3390/publications7020040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
A scholarly communication system needs to register, distribute, certify, archive, and incentivize knowledge production. The current article-based system technically fulfills these functions, but suboptimally. I propose a module-based communication infrastructure that attempts to take a wider view of these functions and optimize the fulfillment of the five functions of scholarly communication. Scholarly modules are conceptualized as the constituent parts of a research process as determined by a researcher. These can be text, but also code, data, and any other relevant pieces of information that are produced in the research process. The chronology of these modules is registered by iteratively linking to each other, creating a provenance record of parent and child modules (and a network of modules). These scholarly modules are linked to scholarly profiles, creating a network of profiles, and a network of how profiles relate to their constituent modules. All these scholarly modules would be communicated on the new peer-to-peer Web protocol Dat, which provides a decentralized register that is immutable, facilitates greater content integrity than the current system through verification, and is open-by-design. Open-by-design would also allow diversity in the way content is consumed, discovered, and evaluated to arise. This initial proposal needs to be refined and developed further based on the technical developments of the Dat protocol, its implementations, and discussions within the scholarly community to evaluate the qualities claimed here. Nonetheless, a minimal prototype is available today, and this is technically feasible.
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24
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Jacob MA. Under repair: A publication ethics and research record in the making. SOCIAL STUDIES OF SCIENCE 2019; 49:77-101. [PMID: 30654711 DOI: 10.1177/0306312718824663] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Based on fieldwork in the Committee on Publication Ethics, this paper offers an analysis of the forms of doings that publication ethics in action can take during what is called the 'Forum', a space where allegations of dubious research conduct get aired and debated between editors and publishers. This article examines recurring motifs within the review of publication practices whose ethics are called into. These motifs include: the shaping of publication ethics as an expertise that can be standardized across locations and disciplines, the separation of the research record from relations that produce it, and the divisibility of the scientific paper. Together these institute an ethics of repair at the centre of the curative enterprise of the Committee on Publication Ethics. Under the language of correcting the literature the members are working out, along with authors, what the research record should be and, inevitably, what it is. In turn, this article elicits new analytical objects that re-describe publication ethics as a form of expertise, beyond (and despite) the rehearsed axioms of this now well-established professional field.
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25
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Smalheiser NR, Luo M, Addepalli S, Cui X. A manual corpus of annotated main findings of clinical case reports. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5290151. [PMID: 30657910 PMCID: PMC6335863 DOI: 10.1093/database/bay143] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 12/13/2018] [Indexed: 12/04/2022]
Abstract
Clinical case reports are the `eyewitness reports’ of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally a case report has a single main finding that represents the reason for writing up the report in the first place. In the present study, we present the results of manual annotation carried out by two individuals on 500 randomly sampled case reports. This corpus contains main finding sentences extracted from title, abstract and full-text of the same article that can be regarded as semantically related and are often paraphrases. The final reconciled corpus of 416 articles comprises an open resource for further study. This is the first step in establishing text mining models and tools that can identify main finding sentences in an automated fashion, and in measuring quantitatively how similar any two main findings are. We envision that case reports in PubMed may be automatically indexed by main finding, so that users can carry out information queries for specific main findings (rather than general topics)—and given one case report, a user can retrieve those having the most similar main findings. The metric of main finding similarity may also potentially be relevant to the modeling of paraphrasing, summarization and entailment within the biomedical literature.
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Affiliation(s)
- Neil R Smalheiser
- Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL, USA
| | - Mengqi Luo
- School of Information Management, Wuhan University, Wuhan, Hubei, China
| | - Sidharth Addepalli
- Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL, USA
| | - Xiaokai Cui
- School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA
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26
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Kilicoglu H. Biomedical text mining for research rigor and integrity: tasks, challenges, directions. Brief Bioinform 2018; 19:1400-1414. [PMID: 28633401 PMCID: PMC6291799 DOI: 10.1093/bib/bbx057] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/10/2017] [Indexed: 01/01/2023] Open
Abstract
An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted because of problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the manifestation of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part toward enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can support tools that promote responsible research practices, providing significant benefits for the biomedical research enterprise.
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Affiliation(s)
- Halil Kilicoglu
- Lister Hill National Center for Biomedical Communications, US National Library of Medicine
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27
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Natsiavas P, Boyce RD, Jaulent MC, Koutkias V. OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies. Front Pharmacol 2018; 9:609. [PMID: 29997499 PMCID: PMC6028717 DOI: 10.3389/fphar.2018.00609] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 05/21/2018] [Indexed: 12/27/2022] Open
Abstract
Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present OpenPVSignal, a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. OpenPVSignal is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. OpenPVSignal is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of OpenPVSignal and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of OpenPVSignal is demonstrated by using PV signal information published in: (a) the World Health Organization's Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.
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Affiliation(s)
- Pantelis Natsiavas
- Centre for Research & Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece.,Lab of Computing, Medical Informatics & Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Richard D Boyce
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Marie-Christine Jaulent
- Institut National de la Santé et de la Recherche Médicale, U1142, LIMICS, Paris, France.,Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, Paris, France.,Université Paris 13, Sorbonne Paris Cité, UMR_S 1142, LIMICS, Villetaneuse, France
| | - Vassilis Koutkias
- Centre for Research & Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece.,Lab of Computing, Medical Informatics & Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Raciti D, Yook K, Harris TW, Schedl T, Sternberg PW. Micropublication: incentivizing community curation and placing unpublished data into the public domain. Database (Oxford) 2018; 2018:4917853. [PMID: 29688367 PMCID: PMC5836261 DOI: 10.1093/database/bay013] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 01/10/2018] [Accepted: 01/18/2018] [Indexed: 12/27/2022]
Abstract
Large volumes of data generated by research laboratories coupled with the required effort and cost of curation present a significant barrier to inclusion of these data in authoritative community databases. Further, many publicly funded experimental observations remain invisible to curation simply because they are never published: results often do not fit within the scope of a standard publication; trainee-generated data are forgotten when the experimenter (e.g. student, post-doc) leaves the lab; results are omitted from science narratives due to publication bias where certain results are considered irrelevant for the publication. While authors are in the best position to curate their own data, they face a steep learning curve to ensure that appropriate referential tags, metadata, and ontologies are applied correctly to their observations, a task sometimes considered beyond the scope of their research and other numerous responsibilities. Getting researchers to adopt a new system of data reporting and curation requires a fundamental change in behavior among all members of the research community. To solve these challenges, we have created a novel scholarly communication platform that captures data from researchers and directly delivers them to information resources via Micropublication. This platform incentivizes authors to publish their unpublished observations along with associated metadata by providing a deliberately fast and lightweight but still peer-reviewed process that results in a citable publication. Our long-term goal is to develop a data ecosystem that improves reproducibility and accountability of publicly funded research and in turn accelerates both basic and translational discovery. Database URL www.micropublication.org.
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Affiliation(s)
- Daniela Raciti
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA 91125, USA
| | - Karen Yook
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA 91125, USA
| | - Todd W Harris
- Informatics and Bio-computing Platform, Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Tim Schedl
- Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Paul W Sternberg
- Division of Biology and Biological Engineering 156-29, California Institute of Technology, Pasadena, CA 91125, USA
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29
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Affiliation(s)
- Gianmaria Silvello
- Department of Information Engineering; University of Padua; Via Gradenigo 6/b, Padua Italy
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31
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Gupta R, Mantri SS. Biomolecular Relationships Discovered from Biological Labyrinth and Lost in Ocean of Literature: Community Efforts Can Rescue Until Automated Artificial Intelligence Takes Over. Front Genet 2016; 7:46. [PMID: 27066067 PMCID: PMC4814459 DOI: 10.3389/fgene.2016.00046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 03/15/2016] [Indexed: 11/30/2022] Open
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33
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Livingston KM, Bada M, Baumgartner WA, Hunter LE. KaBOB: ontology-based semantic integration of biomedical databases. BMC Bioinformatics 2015; 16:126. [PMID: 25903923 PMCID: PMC4448321 DOI: 10.1186/s12859-015-0559-3] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Accepted: 03/30/2015] [Indexed: 04/04/2023] Open
Abstract
Background The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources. Results We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license. Conclusions KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0559-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kevin M Livingston
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Michael Bada
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - William A Baumgartner
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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Bölling C, Weidlich M, Holzhütter HG. SEE: structured representation of scientific evidence in the biomedical domain using Semantic Web techniques. J Biomed Semantics 2014; 5:S1. [PMID: 25093070 PMCID: PMC4108886 DOI: 10.1186/2041-1480-5-s1-s1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Accounts of evidence are vital to evaluate and reproduce scientific findings and integrate data on an informed basis. Currently, such accounts are often inadequate, unstandardized and inaccessible for computational knowledge engineering even though computational technologies, among them those of the semantic web, are ever more employed to represent, disseminate and integrate biomedical data and knowledge. RESULTS We present SEE (Semantic EvidencE), an RDF/OWL based approach for detailed representation of evidence in terms of the argumentative structure of the supporting background for claims even in complex settings. We derive design principles and identify minimal components for the representation of evidence. We specify the Reasoning and Discourse Ontology (RDO), an OWL representation of the model of scientific claims, their subjects, their provenance and their argumentative relations underlying the SEE approach. We demonstrate the application of SEE and illustrate its design patterns in a case study by providing an expressive account of the evidence for certain claims regarding the isolation of the enzyme glutamine synthetase. CONCLUSIONS SEE is suited to provide coherent and computationally accessible representations of evidence-related information such as the materials, methods, assumptions, reasoning and information sources used to establish a scientific finding by adopting a consistently claim-based perspective on scientific results and their evidence. SEE allows for extensible evidence representations, in which the level of detail can be adjusted and which can be extended as needed. It supports representation of arbitrary many consecutive layers of interpretation and attribution and different evaluations of the same data. SEE and its underlying model could be a valuable component in a variety of use cases that require careful representation or examination of evidence for data presented on the semantic web or in other formats.
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Affiliation(s)
- Christian Bölling
- Institute of Biochemistry, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Weidlich
- Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
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Ansari S, Binder J, Boue S, Di Fabio A, Hayes W, Hoeng J, Iskandar A, Kleiman R, Norel R, O'Neel B, Peitsch MC, Poussin C, Pratt D, Rhrissorrakrai K, Schlage WK, Stolovitzky G, Talikka M. On Crowd-verification of Biological Networks. Bioinform Biol Insights 2013; 7:307-25. [PMID: 24151423 PMCID: PMC3798292 DOI: 10.4137/bbi.s12932] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.
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