Author

Anna Posfai

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

Click to filter author papers

Biophysics· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 2 Research Focus:benchmarking· 1 Approach:machine learning (classical)· 1 Research Focus:software / tool· 1

Frequent Co-Authors

David M. McCandlishiD· 3 shared Justin B. KinneyiD· 3 shared Ammar Tareen· 1 shared Juannan ZhouiD· 1 shared Mahdi KooshkbaghiiD· 1 shared William T. IrelandiD· 1 shared

Activity by Year

2022-2025 · peak 2/yr

Co-Author Collaboration Network

6 top collaborators · 12 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersBiophysics

David M. McCandlishORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Symmetry, gauge freedoms, and the interpretability of sequence-function relationships

Anna Posfai, David M. McCandlish, Justin B. Kinney

This paper analyzes gauge freedoms in sequence-function models and shows their relationship to symmetry and parameter interpretability.

Physical Review Research · Apr 2, 2025Peer-reviewedBiophysics

Gauge fixing for sequence-function relationships

Anna Posfai, Juannan Zhou, David M. McCandlish, Justin B. Kinney

This paper introduces a family of gauges for sequence-function models to remove gauge freedoms and improve model interpretation.

PLoS Computational Biology · Mar 20, 2025Peer-reviewedBiophysics

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