PNNL

Daniel Claborne is a data scientist in the Applied Statistics and Computational Modeling group. His work focuses on application/package development and statistical analysis of mass-spectrometry data, as well as machine learning applications in the computer vision, audio, and natural language processing domains. He was the lead developer of the FREDA Shiny application for exploratory analysis of Fourier-transform mass-spectrometry data and is currently developing the integrated-PMart Shiny application for ‘omics-data analysis. 

• R – statistical analysis and visualization, Shiny web-app development
• Python – machine learning for computer vision, audio, and NLP

• B.S., Economics – Oregon State University
• M.S, Statistics – Oregon State University

2020

• Bramer L.M., A.M. White, K.G. Stratton, A.M. Thompson, D. Claborne, K. Hofmockel, et al. 2020. “ftmsRanalysis: An R Package for Exploratory Data Analysis and Interactive Visualization of FT-MS Data.” PLoS Comput Biol 16(3): e1007654. https://doi.org/10.1371/journal.pcbi.1007654
• Claborne D.M., K. Pazdernik, S.J. Rysavy, and M.J. Henry. 2020. “Video Summarization Using Deep Action Recognition Features and Robust Principal Components Analysis.” In World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2020. PNNL-SA-152604.

2019

• Stratton K.G., B-J.M. Webb-Robertson, L.A. McCue, B. Stanfill, D. Claborne, I. Godinez, T. Johansen, A.M. Thompson, K.E. Burnum-Johnson, K.M. Waters, and L.M. Bramer. 2019. “pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data.” Journal of Proteome Research 18 (3): 1418–1425. DOI: 10.1021/acs.jproteome.8b00760