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Published March 2019 | Supplemental Material + Submitted + Published
Journal Article Open

Harmonizing semantic annotations for computational models in biology


Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Additional Information

© The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Received: 15 May 2018; Revision Received: 08 August 2018; Accepted: 17 August 2018; Published: 21 November 2018. This work was supported by the National Institutes of Health (grant number LM011969 to M.LN., D.L.C. and J.H.G.; GM109824 to M.L.N.; GM070923 to A.D., M.H. and N.L.; MH106674 and EB021711 to S.C.; GM123032 and EB023912 to H.M.S.); the German Federal Ministry of Education and Research (BMBF) (SEMS 031 6194 to D.W.; LiSyM 031L0054 to M.K.; LiSyM, 031L0056 to M.G.); the Aotearoa Foundation Fellowship (to D.N. and M.T.C.); the Medical Technologies Centre of Research Excellence (to R.K. and K.A.); the University of Auckland Faculty Research Development Fund (grant number 3714350 to R.K. and K.A.); the European Commission (grant number 731001 to M.de A.); the German Federal Ministry for Economic Affairs and Energy (NormSys 01FS14019 to M.G.); the Klaus Tschira Foundation (to M.G.); the United States of America's National Science Foundation (grant numbers CCF-1748200, CCF-1218095, and DBI-1356041 to C.M.); the BE-Basic Foundation (grant number F08.005.001 to B.G.O.); the Department of Science and Technology/National Research Foundation in South Africa (grant number NRF-SARCHI-82813 to J.L.S.); the Biotechnology and Biological Sciences Research Council (grant numbers BBG0102181, BB/I004637/1 and BB/M013189/1 to J.L.S.) and Wellcome Trust (grant number 101445 to P.G.) in the UK; the Norwegian University of Science and Technology's Strategic Research Area 'NTNU Health' (to V.T.); and ERACoSysMed (grant ID COLOSYS to V.T.); Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies. The authors represent teams developing various standards for biological modeling and are participants in the COmputational Modeling in BIology NEtwork (COMBINE) community.

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Published - bby087.pdf

Submitted - 246470.full.pdf

Supplemental Material - 246470-1.zip

Supplemental Material - 246470-2.zip


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August 22, 2023
October 23, 2023