S. Christiaens
ORCID:
0000-0002-9584-9618
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About
Contact & Profiles
A5024057402
Research Areas
- Genetics, Bioinformatics, and Biomedical Research
- Genomics and Rare Diseases
- Big Data and Business Intelligence
- Business Process Modeling and Analysis
- Biomedical Text Mining and Ontologies
- Collaboration in agile enterprises
- Semantic Web and Ontologies
Centerforce
2022
Vrije Universiteit Amsterdam
2010
Nicole Vasilevsky
Nicolas Matentzoglu
Sabrina Toro
Joseph Eugene Flack
Harshad Hegde
and 81
more
Deepak Unni
Gioconda Alyea
Joanna Amberger
Lawrence Babb
James P. Balhoff
Taylor I. Bingaman
Gully Burns
Orion J. Buske
Tiffany J Callahan
Leigh Carmody
Paula Carrio-Cordo
Lauren Chan
George S Chang
S. Christiaens
Michel Dumontier
Laura Failla
May J Flowers
H. Alpha Garrett
Jennifer Goldstein
Dylan Gration
Tudor Groza
Marc Hanauer
Nomi L. Harris
Jason A. Hilton
Daniel Himmelstein
Charles Tapley Hoyt
Megan Kane
Sebastian Köhler
David Lagorce
Abbe Lai
Martin Larralde
Antonia Lock
Irene López Santiago
Donna Maglott
Adriana J Malheiro
Birgit H M Meldal
Monica Muñoz‐Torres
Tristan Nelson
F. W. Nicholas
David Ochoa
Daniel Olson
Tudor I. Oprea
David Osumi-Sutherland
Helen Parkinson
Zoë May Pendlington
Ana Rath
Heidi L. Rehm
Lyubov Remennik
Erin Rooney Riggs
Paola Roncaglia
Justyne Ross
Marion Shadbolt
Kent Shefchek
Morgan Similuk
Nicholas Sioutos
Damian Smedley
Rachel Sparks
Ray Stefancsik
Ralf Stephan
Andrea L. Storm
Doron Stupp
Gregory S. Stupp
Jagadish Chandrabose Sundaramurthi
Imke Tammen
D. K. C. Tay
Courtney Thaxton
Eloise Valasek
Jordi Valls-Margarit
Alex H. Wagner
Danielle Welter
Patricia L. Whetzel
Lori Whiteman
Valerie Wood
Colleen H. Xu
Andreas Zankl
Xingmin Zhang
Christopher G. Chute
Peter N. Robinson
Chris Mungall
Ada Hamosh
Melissa Haendel
Abstract There are thousands of distinct disease entities and concepts, each which known by different sometimes contradictory names. The lack a unified system for managing these poses major challenge both machines humans that need to harmonize information better predict causes treatments disease. Mondo Disease Ontology is an open, community-driven ontology integrates key medical biomedical terminologies, supporting data integration improve diagnosis, treatment, translational research....
10.1101/2022.04.13.22273750
preprint
EN
cc-by
medRxiv (Cold Spring Harbor Laboratory)
2022-04-16
10.1016/j.compind.2010.05.005
article
EN
Computers in Industry
2010-07-07
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