RT Journal Article
SR Electronic
T1 Harmonizing semantic annotations for computational models in biology
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 246470
DO 10.1101/246470
A1 ML Neal
A1 M König
A1 D Nickerson
A1 G Mısırlı
A1 R Kalbasi
A1 A Dräger
A1 K Atalag
A1 V Chelliah
A1 M Cooling
A1 DL Cook
A1 S Crook
A1 M de Alba
A1 SH Friedman
A1 A Garny
A1 JH Gennari
A1 P Gleeson
A1 M Golebiewski
A1 M Hucka
A1 N Juty
A1 N Le Novère
A1 C Myers
A1 BG Olivier
A1 HM Sauro
A1 M Scharm
A1 JL Snoep
A1 V Touré
A1 A Wipat
A1 O Wolkenhauer
A1 D Waltemath
YR 2018
UL http://biorxiv.org/content/early/2018/01/23/246470.abstract
AB 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 semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.