Author

Lance X. Zhang

Oak Ridge National LaboratoryORCID 0000-0002-3381-9018Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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

Frequent Co-Authors

Alexis N. WilliamsiD· 2 shared Amber N. BibleiD· 2 shared Chanda R. Harris· 2 shared Jennifer L. Morrell‐FalveyiD· 2 shared Lynnicia N. MassenburgiD· 2 shared Rama Krishnan VasudevaniD· 2 shared Scott T. RettereriD· 2 shared Sita Sirisha MadugulaiD· 2 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersMicrobiology

Alexis N. WilliamsORCID

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Author profile →

3 papers

Recent·Most cited

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

Process optimization for enhanced enzymatic nylon deconstruction

Célestin Bourgery, Lance X. Zhang, Dana L. Carper, Muchu Zhou, Robert L. Sacci, Matt Korey, Lei Wang, Jessica H.B. Hemond, John F. Cahill, Vera Bocharova, Joshua K. Michener

This study optimizes reaction conditions for three nylon-degrading enzymes, achieving high product titers for PA6 and PA66 without enzyme engineering or substrate pretreatment, and demonstrates scalability for complex PA-based materials.

bioRxiv · Nov 26, 2025 · PreprintBiochemistry