Author

Nevan Krogan

Gladstone Institutes

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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Bioengineering· 1 Cell Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:machine learning (classical)· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1 Assay:CRISPR screen· 1 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Alexander MarsoniD· 1 shared Alina Sigaeva· 1 shared Amir DailamyiD· 1 shared Amirali Aghazadeh· 1 shared Andrew P. MayiD· 1 shared Antoine ForgetiD· 1 shared Benjamin J. PolaccoiD· 1 shared Christian Metallo· 1 shared

Activity by Year

2018-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperCell Biology

Alexander MarsonORCID

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Author profile →

2 papers

Recent·Most cited

A PERTURBATION CELL ATLAS OF HUMAN INDUCED PLURIPOTENT STEM CELLS

Sami Nourreddine, Yesh Doctor, Amir Dailamy, Antoine Forget, Yi-Hung Lee, Rebecca Chinn, Hammza Khaliq, Benjamin J. Polacco, Monita Muralidharan, Emily Pan, Yifan Zhang, Alina Sigaeva, Jan N. Hansen, Jiahao Gao, Jillian A. Parker, , Prashant Mali

The paper generates a CRISPRi perturbation cell atlas in human induced pluripotent stem cells, identifies novel regulators of pluripotency and metabolism, and proposes a machine learning framework for predicting perturbation recipes.

Nature Biotechnology · Jul 1, 2026Peer-reviewedBioengineering

Systematic characterization of genome editing in primary T cells reveals proximal genomic insertions and enables machine learning prediction of CRISPR-Cas9 DNA repair outcomes

Ryan T. Leenay, Amirali Aghazadeh, Joseph Hiatt, David Tse, Judd F. Hultquist, Nevan Krogan, Zhenqin Wu, Alexander Marson, Andrew P. May, James Zou

This study systematically characterizes genome editing in primary T cells, identifies proximal genomic insertions, and develops a machine learning model to predict CRISPR-Cas9 DNA repair outcomes.

bioRxiv · Aug 31, 2018 · PreprintCell Biology