Author

Robert T. McLaughlin

AbbVie (United States)ORCID 0000-0002-1650-3847Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:benchmarking· 2 Organism:human· 2 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

David L. Masica· 1 shared Hin Ching LoiD· 1 shared Howard J. JacobiD· 1 shared Jacob PfeiliD· 1 shared Josue A. SamayoaiD· 1 shared Kyle D. HalliwilliD· 1 shared Liqian MaiD· 1 shared Maansi AsthanaiD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperGenomics

David L. Masica

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJul 1, 2024

tauX: A Gene Expression Ratio Strategy to Improve Machine Learning Applications in Precision Medicine

Jacob Pfeil, Liqian Ma, Hin Ching Lo…·Kyle D. Halliwill

The paper introduces tauX, a gene expression ratio strategy that improves machine learning performance in precision medicine by refining gene expression features.

DOI 10.1101/2024.07.01.601595

humanbulk RNA-seq

𝕏 PostBluesky

GenomicsbioRxivPreprintDec 7, 2021

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…·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.

1 citation

humanwhole-exome sequencing

𝕏 PostBluesky