Author

Ammar Tareen

Cold Spring Harbor Laboratory

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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Bioinformatics· 3 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Assay:DNA methylation· 2 Research Focus:software / tool· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Assay:protein sequence· 1

Frequent Co-Authors

Justin B. KinneyiD· 4 shared William T. IrelandiD· 2 shared Anna Posfai· 1 shared Bruce StillmaniD· 1 shared Christian SpeckiD· 1 shared David M. McCandlishiD· 1 shared Huilin LiiD· 1 shared Leemor Joshua‐ToriD· 1 shared

Activity by Year

2019-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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4 shared papersBioinformatics

Justin B. KinneyORCID

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4 papers

Recent·Most cited

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

Ammar Tareen, Mahdi Kooshkbaghi, Anna Posfai, William T. Ireland, David M. McCandlish, Justin B. Kinney

MAVE-NN learns genotype-phenotype maps from MAVE data by using a neural network-based information-theoretic framework, enabling biophysically interpretable models.

Genome biology · Apr 15, 2022Peer-reviewedBioinformatics

Evolution of DNA Replication Origin Specification and Gene Silencing Mechanisms

Yixin Hu, Ammar Tareen, Yi-Jun Sheu, William T. Ireland, Christian Speck, Huilin Li, Leemor Joshua‐Tor, Justin B. Kinney, Bruce Stillman

The paper identifies sequence-specificity in DNA replication origins mediated by Orc4 α-helix in S. cerevisiae and links its evolution to gene silencing mechanisms.

Nature Communications · Oct 14, 2020Peer-reviewedMolecular Biology

Biophysical models of cis-regulation as interpretable neural networks

Ammar Tareen, Justin B. Kinney

This paper introduces biophysical models of cis-regulation as interpretable neural networks, demonstrating their inference from MPRAs data.

bioRxiv · Feb 8, 2020 · PreprintBioinformatics

Logomaker: beautiful sequence logos in Python

Ammar Tareen, Justin B. Kinney

Logomaker is a Python API for generating publication-quality sequence logos from numerical matrices, with support for customization and vector graphics.

Bioinformatics · Dec 6, 2019Peer-reviewedBioinformatics