Author

Faiz W. Rizvi

University of CincinnatiORCID 0000-0001-8644-4697Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Assay:ATAC-seq· 1 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Anthony T. BejjaniiD· 1 shared Artem BarskiiD· 1 shared Balaji IyeriD· 1 shared Benjamin WronowskiiD· 1 shared Emily R. MiraldiiD· 1 shared Joseph A. Wayman· 1 shared Leah Claire KottyaniD· 1 shared Matthew T. WeirauchiD· 1 shared

Activity by Year

2023-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Anthony T. BejjaniORCID

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1 paper

Recent·Most cited

maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

Tareian A. Cazares, Faiz W. Rizvi, Balaji Iyer, Xiaoting Chen, Michael Kotliar, Anthony T. Bejjani, Joseph A. Wayman, Omer A. Donmez, Benjamin Wronowski, Sreeja Parameswaran, Leah Claire Kottyan, Artem Barski, Matthew T. Weirauch, V. B. Surya Prasath, Emily R. Miraldi

maxATAC provides deep learning models for predicting transcription factor binding sites from ATAC-seq data, improving prediction in primary and single-cell contexts.

PLoS Computational Biology · Jan 31, 2023Peer-reviewedBioinformatics