Author

J. Hulquist

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Cell Biology· 1

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Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

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A. Aghazadeh· 1 shared A. Marson· 1 shared A. P. May· 1 shared D. Tse· 1 shared J. Hiatt· 1 shared J. Zou· 1 shared N. Krogan· 1 shared R. T. Leenay· 1 shared

Activity by Year

2018-2018 · peak 1/yr

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9 top collaborators · 36 mutual co-author links

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1 shared paperCell Biology

A. Aghazadeh

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Recent·Most cited

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

R. T. Leenay, A. Aghazadeh, J. Hiatt, D. Tse, J. Hulquist, N. Krogan, Z. Wu, A. Marson, A. P. May, J. 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