Author

Emily C. Hinds

University of Wisconsin–Madison

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· 2 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:protein sequence· 2 Research Focus:sequence-to-function modeling· 2 Research Focus:benchmarking· 1 Research Focus:protein design· 1 Research Focus:protein structure prediction· 1 Assay:proteomics· 1

Frequent Co-Authors

Hyebin SongiD· 2 shared Philip A. RomeroiD· 2 shared Bennett J. Bremer· 1 shared Chase R. FreschliniD· 1 shared Garvesh RaskuttiiD· 1 shared Ivan JunieriD· 1 shared Marion ChauveauiD· 1 shared Olivier RivoireiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersBioinformatics

Hyebin SongORCID

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Author profile →

3 papers

Recent·Most cited

Improved inference of multiscale sequence statistics in generative protein models

Marion Chauveau, Yaakov Kleeorin, Emily C. Hinds, Ivan Junier, Rama Ranganathan, Olivier Rivoire

This paper introduces a new regularization strategy for generative protein models that improves the inference of multiscale sequence statistics, enabling the generation of functional and diverse protein sequences.

bioRxiv · Aug 24, 2026 · PreprintSystems Biology

Inferring protein fitness landscapes from laboratory evolution experiments

Sameer D’Costa, Emily C. Hinds, Chase R. Freschlin, Hyebin Song, Philip A. Romero

This paper develops a statistical learning framework to infer protein fitness landscapes from laboratory evolution data and applies it to DHFR evolution to understand epistasis and design future proteins.

PLoS Computational Biology · Mar 1, 2023Peer-reviewedBioinformatics

Inferring protein sequence-function relationships with large-scale positive-unlabeled learning

Hyebin Song, Bennett J. Bremer, Emily C. Hinds, Garvesh Raskutti, Philip A. Romero

This paper develops a positive-unlabeled learning framework to infer sequence-function relationships from DMS data and applies it to design stabilized enzymes.

Cell Systems · Nov 18, 2020Peer-reviewedBioinformatics