Author

C. Bobak

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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Pathology· 2

Signature Assays, Organisms & Methods

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Organism:human· 2 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Assay:spatial transcriptomics· 1 Approach:statistical model· 1

Frequent Co-Authors

J. Levy· 2 shared A. Suriawinata· 1 shared B. C. Christensen· 1 shared B. Christensen· 1 shared B. Ren· 1 shared E. M. Veziroglu· 1 shared G. J. Tsongalis· 1 shared J. O'Malley· 1 shared

Activity by Year

2021-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersPathology

J. Levy

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Author profile →

2 papers

Recent·Most cited

Estimating the Inter- and Intra-Rater Reliability for NASH Fibrosis Staging in the Presence of Bridge Ordinal Ratings with Hierarchical Bridge Category Models

J. Levy, C. Bobak, N. Azizgolshani, X. Liu, B. Ren, M. Lisovsky, A. Suriawinata, B. Christensen, J. O'Malley, L. Vaickus

This paper develops hierarchical Bayesian models to assess inter- and intra-rater reliability for NASH fibrosis staging, showing improved precision over ad hoc methods.

bioRxiv · Oct 27, 2021 · PreprintPathology

Mixed Effects Machine Learning Models for Colon Cancer Metastasis Prediction using Spatially Localized Immuno-Oncology Markers

J. Levy, C. Bobak, M. Nasir-Moin, E. M. Veziroglu, S. M. Palisoul, R. E. Barney, L. A. Salas, B. C. Christensen, G. J. Tsongalis, L. J. Vaickus

This study uses mixed-effects machine learning to predict colon cancer metastasis by analyzing spatially localized immuno-oncology markers in tumor tissue.

bioRxiv · Aug 1, 2021Pathology