Author

Amin Espah Borujeni

Massachusetts Institute of TechnologyORCID 0000-0002-3036-7183Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 3 Assay:bulk RNA-seq· 2 Organism:E. coli· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1

Frequent Co-Authors

Christopher A. VoigtiD· 2 shared Diveena Becker· 2 shared Enoch YeungiD· 2 shared Joshua A. UrrutiaiD· 2 shared Paul MaschhoffiD· 2 shared Yuval DorfaniD· 2 shared Alexander W. CristofaroiD· 1 shared Aqib HasnainiD· 1 shared

Activity by Year

2017-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 34 mutual co-author links

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2 shared papersSystems Biology

Christopher A. VoigtORCID

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3 papers

Recent·Most cited

Disentangling gene expression burden identifies generalizable phenotypes induced by synthetic gene networks

Aqib Hasnain, Amin Espah Borujeni, Yongjin Park, Diveena Becker, Paul Maschhoff, Joshua A. Urrutia, Linus Rydell, Shara Balakrishnan, Yuval Dorfan, Christopher A. Voigt, Enoch Yeung

The paper identifies synthetic gene network-induced phenotypes by analyzing gene expression in E. coli and validates them through antibiotic resistance experiments.

bioRxiv · Jun 29, 2023 · PreprintSystems 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

Precise Quantification of Translation Inhibition by mRNA Structures that Overlap with the Ribosomal Footprint in N-terminal Coding Sequences

Amin Espah Borujeni, Daniel P. Cetnar, Iman Farasat, Ashlee Smith, Natasha Lundgren, Howard M. Salis

The paper quantifies how mRNA structures overlapping with the ribosomal footprint inhibit translation in E. coli, showing up to 1410-fold repression.

Nucleic Acids Research · Jan 30, 2017Peer-reviewedGenetics