Author

Taylor Firman

Children's Hospital Colorado

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Genomics· 1 Synthetic Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Assay:protein sequence· 2 Research Focus:database / resource· 1 Assay:GWAS summary statistics· 1 Organism:human· 1

Frequent Co-Authors

Alisa Gaskell· 2 shared Michael S. BradshawiD· 2 shared Ryan M. LayeriD· 2 shared Anar AmgalaniD· 1 shared Bailey K. FosdickiD· 1 shared Connor P. Gibbs· 1 shared Jishnu RaychaudhuriiD· 1 shared Kingshuk GhoshiD· 1 shared

Activity by Year

2018-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersGenomics

Alisa Gaskell

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Rapid, Reliable, and Interpretable CNV Curation Visualizations for Diagnostic Settings with SeeNV

Michael S. Bradshaw, Jishnu Raychaudhuri, Lachlan Murphy, Rebecca Barnard, Taylor Firman, Alisa Gaskell, Ryan M. Layer

SeeNV is a tool for rapid, reliable, and interpretable CNV curation in diagnostic settings, achieving high precision and recall with minimal user time.

Journal of Molecular Diagnostics · Mar 3, 2025Peer-reviewedGenomics

Hypothesis Generation For Rare and Undiagnosed Diseases Through Clustering and Classifying Time-Versioned Biological Ontologies

Michael S. Bradshaw, Connor P. Gibbs, Skylar Martin, Taylor Firman, Alisa Gaskell, Bailey K. Fosdick, Ryan M. Layer

This paper identifies potential gene-to-phenotype connections for undiagnosed diseases by clustering time-versioned biological ontologies and analyzing network data.

PLoS ONE · Dec 26, 2024Peer-reviewedBioinformatics

Maximum Caliber can build and infer models of oscillation in a three-gene feedback network

Taylor Firman, Anar Amgalan, Kingshuk Ghosh

The paper uses Maximum Caliber to infer parameters of a three-gene feedback network from synthetic protein time traces, showing that a minimal model captures key features while more complex models improve predictions.

The Journal of Physical Chemistry B · Dec 3, 2018Peer-reviewedSynthetic Biology