Author

W. Bachman

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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Bioengineering· 2 Microbiology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:clustering· 2 Approach:deep learning· 2 Assay:imaging· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

D. S. Long· 2 shared A. Iovleva· 1 shared B. Ahn· 1 shared C. McElheny· 1 shared D. B. Stolz· 1 shared D. Van Tyne· 1 shared E. Papke· 1 shared H. Penaloza· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersBioengineering

D. S. Long

Author profile →

3 papers

Recent·Most cited

In vivo evolution of a Klebsiella pneumoniae capsule defect promotes complement-mediated opsono-phagocytosis and persistence during recurrent infection

W. Bain, B. Ahn, H. Penaloza, C. McElheny, N. Tolman, R. van der Geest, S. Gonzalez-Ferrer, N. Chen, X. An, R. Hosuru, M. Tabary, E. Papke, N. Kohli, N. Farooq, W. Bachman, , J. Lee

The paper identifies a capsule defect in K. pneumoniae that enhances complement-mediated opsonophagocytosis and persistence during recurrent infections.

bioRxiv · May 31, 2023 · PreprintMicrobiology

A Machine Learning Framework to Predict Subcellular Morphology of Endothelial Cells for Digital Twin Generation

M. Contreras, R. W. Hafenstine, W. Bachman, D. S. Long

This paper develops a machine learning framework to predict 3D subcellular morphology of endothelial cells and generates digital twins with high accuracy.

bioRxiv · Mar 21, 2022 · PreprintBioengineering

Discrete Protein Metric (DPM): A new image similarity metric to calculate accuracy of deep learning-generated cell focal adhesion predictions

M. A. Contreras, W. Bachman, D. S. Long

This paper introduces the Discrete Protein Metric (DPM) to assess the accuracy of deep learning-generated cell focal adhesion predictions by evaluating distribution, shape/size, and angle differences.

bioRxiv · Dec 10, 2021Bioengineering