Author

Chanda R. Harris

Oak Ridge National Laboratory

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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Microbiology· 3

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 3 Research Focus:cell type annotation· 3 Assay:cryo-EM· 3 Approach:deep learning· 3 Research Focus:benchmarking· 2 Research Focus:clustering· 1 Research Focus:dimensionality reduction· 1 Approach:experimental assay· 1

Frequent Co-Authors

Alexis N. WilliamsiD· 3 shared Amber N. BibleiD· 3 shared Jennifer L. Morrell‐FalveyiD· 3 shared Lynnicia N. MassenburgiD· 3 shared Rama Krishnan VasudevaniD· 3 shared Scott T. RettereriD· 3 shared Sita Sirisha MadugulaiD· 3 shared Spenser R. BrowniD· 3 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersMicrobiology

Alexis N. WilliamsORCID

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Author profile →

3 papers

Recent·Most cited

TileBac: A Benchmark CryoEM Dataset of Bacteria in Ultralow-Dose Montage Tiles

Lynnicia N. Massenburg, Sita Sirisha Madugula, Spenser R. Brown, Amber N. Bible, Chanda R. Harris, Scott T. Retterer, Jennifer L. Morrell‐Falvey, Rama Krishnan Vasudevan, Alexis N. Williams

TileBac provides a benchmark dataset for segmenting bacterial cell envelopes in ultralow-dose cryoEM images, showing foundation models outperform convolutional neural networks.

bioRxiv · Jun 9, 2026 · PreprintMicrobiology

Top Model Decision Tree: Selecting Segmentation Models for Reliable Quantitative Analysis in Low- and Ultralow-Dose CryoEM

Lynnicia N. Massenburg, Sita Sirisha Madugula, Spenser R. Brown, Amber N. Bible, Chanda R. Harris, Lance X. Zhang, Kiara K Parker, Scott T. Retterer, Jennifer L. Morrell‐Falvey, Rama Krishnan Vasudevan, Alexis N. Williams

This paper evaluates segmentation models for low- and ultralow-dose cryoEM images, finding that model choice significantly affects downstream quantitative measurements.

bioRxiv · Jun 6, 2026 · PreprintMicrobiology

Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

Sita Sirisha Madugula, Lynnicia N. Massenburg, Spenser R. Brown, Amber N. Bible, Chanda R. Harris, Lance X. Zhang, Kiara Parker, Scott T. Retterer, Jennifer L. Morrell‐Falvey, Rama Krishnan Vasudevan, Alexis N. Williams

This paper develops an AI-driven workflow for automated analysis of bacterial ultrastructure in cryoEM data, enabling rapid and accurate quantification of membrane thickness, flagella length, and bacterial arrangement.

bioRxiv · Dec 8, 2025 · PreprintMicrobiology