Author

David L. Masica

Johns Hopkins University

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· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:variant effect prediction· 2 Assay:whole-exome sequencing· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Organism:human· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Christopher Blair DouvilleiD· 1 shared Collin J. TokheimiD· 1 shared Howard J. JacobiD· 1 shared Kyle Moad· 1 shared Maansi AsthanaiD· 1 shared Marc A. Di Meo· 1 shared Michaël RyaniD· 1 shared Michele CeccarelliiD· 1 shared

Activity by Year

2017-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperBioinformatics

Christopher Blair DouvilleORCID

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2 papers

Recent·Most cited

Attentive deep learning-based tumor-only somatic mutation classifier achieves high accuracy agnostic of tissue type and capture kit.

Robert T. McLaughlin, Maansi Asthana, Marc A. Di Meo, Michele Ceccarelli, Howard J. Jacob, David L. Masica

The paper develops a deep learning classifier to distinguish somatic and germline mutations in tumor-only samples, achieving high accuracy across tissue types and reducing racial bias in TMB estimates.

bioRxiv · Dec 7, 2021 · PreprintGenomics

CRAVAT 4: Cancer-Related Analysis of Variants Toolkit

David L. Masica, Christopher Blair Douville, Collin J. Tokheim, Rohit Bhattacharya, RyangGuk Kim, Kyle Moad, Michaël Ryan, Rachel Karchin

CRAVAT 4 provides tools for analyzing and interpreting somatic mutations in cancer studies, offering annotation, prioritization, and visualization with a web interface.

Cancer Research · Oct 31, 2017Peer-reviewedBioinformatics