Author

Elaine C. Meng

University of California, San Francisco

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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Bioinformatics· 1 Biophysics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Assay:cryo-EM· 1 Approach:deep learning· 1 Organism:human· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Airlie J. McCoyiD· 1 shared Alexander R. PicoiD· 1 shared Billy K. PooniD· 1 shared Dorothee Claudia LiebschneriD· 1 shared Eric F. Pettersen· 1 shared Gary D. BaderiD· 1 shared J. Javier Díaz-MejíaiD· 1 shared John H. MorrisiD· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBiophysics

Airlie J. McCoyORCID

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2 papers

Recent·Most cited

Likelihood-based interactive local docking into cryo-EM maps in ChimeraX

Randy J. Read, Eric F. Pettersen, Airlie J. McCoy, Tristan Ian Croll, Thomas C. Terwilliger, Billy K. Poon, Elaine C. Meng, Dorothee Claudia Liebschner, Paul D. Adams

The paper introduces a likelihood-based local docking method for cryo-EM maps, improving speed and sensitivity when component locations are known, implemented in ChimeraX.

Acta Crystallographica Section D Structural Biology · Jul 21, 2024Peer-reviewedBiophysics

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

J. Javier Díaz-Mejía, Elaine C. Meng, Alexander R. Pico, Sonya A. MacParland, Troy Ketela, Trevor J. Pugh, Gary D. Bader, John H. Morris

This study benchmarks four methods for assigning cell type labels to cell clusters in scRNA-seq data, showing high performance but variability in precision-recall across methods and datasets.

F1000Research · Mar 15, 2019Peer-reviewedBioinformatics