Author

Roberto Ibañez

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

Bioinformatics· 1 Synthetic Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:protein sequence· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:protein design· 1 Research Focus:protein structure prediction· 1 Research Focus:sequence-to-function modeling· 1 Research Focus:software / tool· 1 Approach:statistical model· 1

Frequent Co-Authors

Michael P. DunneiD· 2 shared Gabriel A. Orellana· 1 shared Javier Caceres-Delpiano· 1 shared Javier Caceres‐DelpianoiD· 1 shared Leonardo Alvarez· 1 shared Leonardo Álvarez· 1 shared Pedro A. Retamal· 1 shared Simon Correa· 1 shared

Activity by Year

2021-2021 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 16 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

2 shared papersBioinformatics

Michael P. DunneORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Protein sequence sampling and prediction from structural data

Gabriel A. Orellana, Javier Caceres-Delpiano, Roberto Ibañez, Michael P. Dunne, Leonardo Alvarez

The paper improves a model for predicting protein sequences from structures, achieving higher accuracy and showing sampled sequences fold into similar structures.

bioRxiv · Sep 6, 2021 · PreprintBioinformatics

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

Javier Caceres‐Delpiano, Roberto Ibañez, Simon Correa, Michael P. Dunne, Pedro A. Retamal, Leonardo Álvarez

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

bioRxiv · Jun 29, 2021 · PreprintSynthetic Biology