Author

Alisa Gaskell

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

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

Michael S. BradshawiD· 3 shared Ryan M. LayeriD· 3 shared Taylor Firman· 2 shared Anton Avramov· 1 shared Bailey K. FosdickiD· 1 shared Connor P. Gibbs· 1 shared Jishnu RaychaudhuriiD· 1 shared Lachlan MurphyiD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBioinformatics

Michael S. BradshawORCID

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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

The effects of biological knowledge graph topology on embedding-based link prediction

Michael S. Bradshaw, Anton Avramov, Alisa Gaskell, Ryan M. Layer

The paper evaluates how knowledge graph topology affects link prediction performance in rare disease variant prioritization, finding that a filtered knowledge graph improves model performance.

bioRxiv · Jun 10, 2024 · PreprintBioinformatics