Author

Javier Caceres‐Delpiano

ORCID 0000-0002-8758-1244Unclaimed 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:benchmarking· 2 Assay:protein sequence· 2 Research Focus:software / tool· 2 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Research Focus:protein design· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Michael P. DunneiD· 2 shared Andrey V. KajavaiD· 1 shared Leonardo Álvarez· 1 shared Pedro A. Retamal· 1 shared Roberto Ibañez· 1 shared Simon Correa· 1 shared Valentin GonayiD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 13 mutual co-author links

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2 shared papersBioinformatics

Michael P. DunneORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintFeb 12, 2024

Developing machine-learning-based amyloid predictors with Cross-Beta DB

Valentin Gonay, Michael P. Dunne, Javier Caceres‐Delpiano, Andrey V. Kajava

The paper develops machine learning predictors for amyloid formation using a new database of naturally occurring cross-beta amyloids, showing improved performance over existing methods.

3 citations

protein sequencemachine learning (classical)

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Synthetic BiologybioRxivPreprintJun 29, 2021

Deep learning model for the prediction and classification of protein toxins across all domains of life

Javier Caceres‐Delpiano, Roberto Ibañez, Simon Correa…·Leonardo Álvarez

This paper introduces a deep learning model, ConvTOX, that classifies protein toxins across all domains of life with high accuracy.

1 citation

protein sequencedeep learning

𝕏 PostBluesky