Rebecca Sharp, Adarsh Pyarelal, Benjamin M. Gyori, and et al. Forthcoming. “Eidos, INDRA & Delphi: From Free Text to Executable Causal Models.” In NAACL.
Petar V. Todorov, Benjamin M. Gyori, John A. Bachman, and Peter K. Sorger. 2019. “INDRA-IPM: interactive pathway modeling using natural language with automated assembly.” Bioinformatics, btz289.
Bing Liu, Benjamin M. Gyori, and P. S. Thiagarajan. 2019. “Statistical Model Checking based Analysis of Biological Networks.” arXiv preprint arXiv:1812.01091.
Charles Hoyt, Daniel Domingo-Fernandez, Rana Aldisi, Lingling Xu, Kristian Kolpeja, Sandra Spalek, Esther Wollert, John Bachman, Benjamin Gyori, Patrick Greene, and Martin Hofmann-Apitius. 2019. “Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language.” Database (in print).
Somponnat Sampattavanich, Bernhard Steiert, Bernhard A. Kramer, Benjamin M. Gyori, John G. Albeck, and Peter K. Sorger. 2018. “Encoding Growth Factor Identity in the Temporal Dynamics of FOXO3 under the Combinatorial Control of ERK and AKT Kinases.” Cell Systems, 6, 6, Pp. 664-678.
John A Bachman, Benjamin M Gyori, and Peter K Sorger. 2018. “FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining.” BMC Bioinformatics, 19, 248.Abstract

Background: For automated reading of scientific publications to
extract useful information about molecular mechanisms it is critical that
genes, proteins and other entities be correctly associated with uniform
identifiers, a process known as named entity linking or "grounding.'' Correct
grounding is essential for resolving relationships among mined information,
curated interaction databases, and biological datasets. The accuracy of this
process is largely dependent on the availability of machine-readable resources
associating synonyms and abbreviations commonly found in biomedical literature
with uniform identifiers.

Results: In a task involving automated reading of  ~215,000
articles using the REACH event extraction software we found that grounding was
disproportionately inaccurate for multi-protein families (e.g., "AKT") and
complexes with multiple subunits  (e.g."NF-kappaB'"). To address this
problem we constructed FamPlex, a manually curated resource defining protein
families and complexes as they are commonly encountered  in biomedical text. In
FamPlex the gene-level constituents of families and complexes are defined in a
flexible format allowing for multi-level, hierarchical membership. To create
FamPlex, text strings corresponding to entities were identified empirically
from literature and linked manually to uniform identifiers; these identifiers
were also mapped to equivalent entries in multiple related databases. FamPlex
also includes curated prefix and suffix patterns that improve named entity
recognition and event extraction.  Evaluation of REACH extractions on a test
corpus of ~54,000 articles showed that FamPlex significantly increased
grounding accuracy for families and complexes (from 15% to 71%). The
hierarchical organization of entities in FamPlex also made it possible to
integrate otherwise unconnected mechanistic information across families,
subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM
reading system and the Biocreative VI Bioentity Normalization Task dataset
demonstrated the utility of FamPlex in other settings.

Conclusion: FamPlex is an effective resource for improving named
entity recognition, grounding, and relationship resolution in automated reading
of biomedical text. The content in FamPlex is available in both tabular and
Open Biomedical Ontology formats at under the Creative Commons CC0
license and has been integrated into the TRIPS/DRUM and REACH reading systems.

Benjamin M. Gyori, John A. Bachman, Kartik Subramanian, Jeremy L. Muhlich, Lucian Galescu, and Peter K. Sorger. 11/2017. “From word models to executable models of signaling networks using automated assembly.” Molecular Systems Biology, 13, 11, Pp. 954. Publisher's Version
Bejnamin M Gyori and Daniel Paulin. 2015. “Hypothesis testing for Markov chain Monte Carlo.” Statistics and Computing,, Pp. 1–12.
R. Ramanathan, Yan Zhang, Jun Zhou, Benjamin M. Gyori, Weng-Fai Wong, and P. S. Thiagarajan. 2015. “Parallelized Parameter Estimation of Biological Pathway Models.” Hybrid Systems Biology, 9271, Pp. 37-57.
Yan Zhang, Sriram Sankaranarayanan, and Benjamin M Gyori. 2015. “Simulation-Guided Parameter Synthesis for Chance-Constrained Optimization of Control Systems.” In Proceedings of the IEEE/ACM International Conference on Computer-Aided Design, Pp. 208–215. IEEE Press.
Benjamin M Gyori, Bing Liu, Soumya Paul, R Ramanathan, and PS Thiagarajan. 2015. “Approximate probabilistic verification of hybrid systems.” Hybrid Systems Biology, 9271, Pp. 96–116.
Benjamin M. Gyori, Gireedhar Venkatachalam, P. S. Thiagarajan, David Hsu, and Marie-Veronique Clement. 2014. “OpenComet: an automated tool for comet assay image analysis.” Redox Biology, 2, Pp. 457-465.
Benjamin M Gyori, Daniel Paulin, and Sucheendra K Palaniappan. 2014. “Probabilistic verification of partially observable dynamical systems.” arXiv preprint arXiv:1411.0976.
Benjamin M. Gyori. 2014. “Probabilistic Approaches to Modeling Uncertainty in Biological Pathway Dynamics.” National University of Singapore.
Sucheendra K. Palaniappan, Benjamin M. Gyori, Bing Liu, David Hsu, and P. S. Thiagarajan. 2013. “Statistical model checking based calibration and analysis of bio-pathway models.” In Proceedings of the 11th International Conference on Computational Methods in Systems Biology, CMSB 13. Austria.
Benjamin M Gyori and Daniel Paulin. 2012. “Non-asymptotic confidence intervals for MCMC in practice.” arXiv preprint arXiv:1212.2016.