Author

Hamed Eramian

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· 2 Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Assay:DNA methylation· 1

Frequent Co-Authors

Daniel BryceiD· 2 shared George Zheng· 2 shared Joshua A. UrrutiaiD· 2 shared Mark Weston· 2 shared Matthew W. VaughniD· 2 shared Mohammed EslamiiD· 2 shared Niall I. GaffneyiD· 2 shared Robert C. MoseleyiD· 2 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 42 mutual co-author links

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2 shared papersBioinformatics

Daniel BryceORCID

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Author profile →

3 papers

Recent·Most cited

AutoGater: A Weakly Supervised Neural Network Model to Gate Cells in Flow Cytometric Analyses

M. R. Eslami, Robert C. Moseley, Hamed Eramian, Daniel Bryce, Steven B. Haase

AutoGater is a weakly supervised model that identifies viable cells in flow cytometry data using light-scatter channels, without fluorescent stains.

Scientific Reports · Oct 9, 2024Peer-reviewedBioinformatics

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

Bree Cummins, Justin D. Vrana, Robert C. Moseley, Hamed Eramian, Anastasia Deckard, Pedro Fontanarrosa, Daniel Bryce, Mark Weston, George Zheng, Joshua Nowak, Francis C. Motta, Mohammed Eslami, Kara Layne Johnson, Robert P. Goldman, Chris John Myers, , Steven B. Haase

This paper introduces DART, a DBTL loop for synthetic genetic circuit design, showing that complex circuits can be more robust and reproducible than simpler ones in budding yeast.

Synthetic Biology · Jan 1, 2023Peer-reviewedSynthetic Biology

Prediction of Whole-Cell Transcriptional Response with Machine Learning

Mohammed Eslami, Amin Espah Borujeni, Hamed Eramian, Mark Weston, George Zheng, Joshua A. Urrutia, Carolyn Corbet, Diveena Becker, Paul Maschhoff, Katie J. Clowers, Alexander W. Cristofaro, Hamid Doost Hosseini, David Benjamin Gordon, Yuval Dorfan, Jedediah M. Singer, , Enoch Yeung

The paper introduces a machine learning model to predict whole-cell transcriptional responses to combinations of perturbations using data from single inducers.

Bioinformatics · Sep 22, 2021Peer-reviewedBioinformatics