Author

Maria V. Carreira

Massachusetts Institute of TechnologyORCID 0000-0002-1404-2514Unclaimed ORCID profile · follow to get notified of new preprints

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Maria V. Carreira1 paper · University of Cambridge

Biology Disciplines

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Biochemistry· 2

Signature Assays, Organisms & Methods

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Assay:cryo-EM· 2 Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

Joseph H. DavisiD· 2 shared Laurel F. KinmaniD· 2 shared Andrew V. GrassettiiD· 1 shared Barrett M. PowelliD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

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4 top collaborators · 5 mutual co-author links

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2 shared papersBiochemistry

Joseph H. DavisORCID

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2 papers

Recent·Most cited

BiochemistrybioRxivPreprintMay 11, 2026

Heterogeneous reconstruction algorithms for cryoEM achieve limited particle classification accuracy on real benchmark datasets

Laurel F. Kinman, Andrew V. Grassetti, Maria V. Carreira, Joseph H. Davis

This study evaluates heterogeneous reconstruction algorithms in cryoEM using benchmark datasets with ground-truth labels, finding limited accuracy in particle classification despite resolving structural heterogeneity.

DOI 10.64898/2026.05.08.722747

cryo-EMdeep learning

𝕏 PostBluesky

BiochemistryStructurePeer-reviewedOct 8, 2024

Automated model-free analysis of cryo-EM volume ensembles with SIREn

Laurel F. Kinman, Maria V. Carreira, Barrett M. Powell, Joseph H. Davis

This paper introduces SIREn, a tool for automated analysis of cryo-EM volume ensembles to identify structural states and quantify their occupancies.

5 citations

cryo-EMdeep learning

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