Author

J. Lavista Ferres

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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Biophysics· 1 Ecology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Approach:graph neural network· 1

Frequent Co-Authors

A. Meller· 1 shared F. Oviedo· 1 shared G. R. Bowman· 1 shared J. Borowsky· 1 shared J. LeBien· 1 shared J. M. Lotthammer· 1 shared M. Campos-Cerqueira· 1 shared M. Kshirsagar· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperBiophysics

A. Meller

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2 papers

Recent·Most cited

EcologybioRxivPreprintJul 25, 2023

A comparison of convolutional neural networks and few-shot learning in classifying long-tailed distributed tropical bird songs

M. Zhong, J. LeBien, M. Campos-Cerqueira…·J. Lavista Ferres

This study compares CNN and few-shot learning methods for classifying rare tropical bird songs, finding Siamese Networks perform better with limited data.

DOI 10.1101/2023.07.25.550590

metagenomicsdeep learning

BiophysicsbioRxivPreprintJun 28, 2022

Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

A. Meller, M. Ward, J. Borowsky…·G. R. Bowman

The paper introduces PocketMiner, a graph neural network that predicts cryptic pockets in proteins, identifying them accurately and rapidly, expanding the druggable proteome.

DOI 10.1101/2022.06.28.497399

humanprotein structure