Author

June Shin

Harvard UniversityORCID 0000-0002-0039-7373Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:protein design· 2 Assay:protein sequence· 2 Approach:deep learning· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Debora S. MarksiD· 2 shared Aaron W. KollaschiD· 1 shared Aashish ManglikiD· 1 shared Ada Y. ShawiD· 1 shared Adam J. RiesselmaniD· 1 shared Andrew C. KruseiD· 1 shared Chris SanderiD· 1 shared Conor T. McMahoniD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersBioinformatics

Debora S. MarksORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintSep 28, 2023

Removing bias in sequence models of protein fitness

Ada Y. Shaw, Hansen Spinner, June Shin…·Debora S. Marks

This paper introduces a de-biasing method for sequence models of protein fitness to improve predictions of functional variants far from natural sequences.

11 citations

protein sequencestatistical model

𝕏 PostBluesky

Systems BiologyNature CommunicationsPeer-reviewedApr 23, 2021

Protein Design and Variant Prediction Using Autoregressive Generative Models

Jung-Eun Shin, Adam J. Riesselman, Aaron W. Kollasch…·Debora S. Marks

This paper introduces an alignment-free deep generative model for protein design and variant effect prediction, showing improved performance over existing methods in challenging cases.

319 citationstop 1%

protein sequencedeep learning

𝕏 PostBluesky