Author

Morteza Haghir Chehreghani

Chalmers University of TechnologyORCID 0000-0002-2912-7422Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Assay:DNA methylation· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Research Focus:software / tool· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Aleksej ZelezniakiD· 1 shared Azam Sheikh MuhammadiD· 1 shared Christoph S BörliniD· 1 shared Christos SkrekasiD· 1 shared Devdatt DubhashiiD· 1 shared Florian DavidiD· 1 shared Jan ZrimeciD· 1 shared Jens NielseniD· 1 shared

Activity by Year

2022-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperSynthetic Biology

Aleksej ZelezniakORCID

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1 paper

Recent·Most cited

Supervised generative design of regulatory DNA forgene expression control

Jan Zrimec, Xiaozhi Fu, Azam Sheikh Muhammad, Christos Skrekas, Vykintas Jauniškis, Nora K. Speicher, Christoph S Börlin, Vilhelm Verendel, Morteza Haghir Chehreghani, Devdatt Dubhashi, Verena Siewers, Florian David, Jens Nielsen, Aleksej Zelezniak

This paper introduces ExpressionGAN, a deep learning model that generates functional regulatory DNA with specified expression levels, outperforming random mutagenesis approaches.

Nature Communications · Aug 30, 2022Peer-reviewedSynthetic Biology