Author

Diogo M. Camacho

Harvard UniversityORCID 0000-0002-1971-3524Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Diogo M. Camacho1 paper

Biology Disciplines

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Bioinformatics· 2 Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Organism:multiple species· 1 Research Focus:phylogenetics / evolution· 1 Research Focus:sequence-to-function modeling· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Daniel BojariD· 2 shared James J. CollinsiD· 2 shared Bianca A. Lepe· 1 shared Jacqueline A. ValeriiD· 1 shared Katherine M. CollinsiD· 1 shared Rani K. PowersiD· 1 shared Timothy K. LuiD· 1 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 9 mutual co-author links

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2 shared papersBioinformatics

Daniel BojarORCID

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3 papers

Recent·Most cited

SweetOrigins: Extracting Evolutionary Information from Glycans

Daniel Bojar, Rani K. Powers, Diogo M. Camacho, James J. Collins

This paper develops a deep learning model to extract evolutionary information from glycans, revealing insights into host-pathogen interactions and phenotypic diversity.

bioRxiv · Apr 8, 2020 · PreprintBioinformatics

Using Natural Language Processing to Learn the Grammar of Glycans

Daniel Bojar, Diogo M. Camacho, James J. Collins

This paper develops a language model for glycans, SweetTalk, to analyze substructure motifs, classify linkages, and predict immunogenicity with 92% accuracy.

bioRxiv · Jan 10, 2020 · PreprintBioinformatics

Sequence-to-function deep learning frameworks for synthetic biology

Jacqueline A. Valeri, Katherine M. Collins, Bianca A. Lepe, Timothy K. Lu, Diogo M. Camacho

The paper introduces STORM, a deep learning model to optimize toehold switches by analyzing sequence-to-function relationships in a dataset of 91,534 toehold switches.

bioRxiv · Dec 10, 2019 · PreprintSynthetic Biology