Author

F. Herrera

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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Ecology· 2 Biochemistry· 1 Cell Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Organism:human· 3 Research Focus:cell type annotation· 2 Approach:experimental assay· 2 Assay:imaging· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1 Research Focus:cell signaling & trafficking· 1

Frequent Co-Authors

D. Alcaraz-Segura· 2 shared E. Guirado· 2 shared F. Murtinheira· 2 shared S. Tabik· 2 shared A. Fernandes· 1 shared A. Grimm· 1 shared A. Harari· 1 shared A. M. Rocha· 1 shared

Activity by Year

2018-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 10 mutual co-author links

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2 shared papersEcology

D. Alcaraz-Segura

Mutual co-authors in cluster

Author profile →

5 papers

Recent·Most cited

Subcellular, biochemical and biophysical alterations in two glial cell models of ARSACS

F. Murtinheira, A. S. Boasinha, J. Belo, L. Macedo, E. Farsetti, T. T. Robalo, V. M. Torres, F. R. Pinto, A. Fernandes, P. Nascimento, M. S. Rodrigues, F. Herrera

This study examines subcellular, biochemical, and biophysical changes in two glial cell models of ARSACS, showing similar disruptions in intermediate filaments, organelle distribution, and autophagy.

bioRxiv · Apr 15, 2024 · PreprintBiochemistry

Tumor Microenvironment Cellular Crosstalk Predicts Response to Adoptive TIL Therapy in Melanoma

D. Barras, E. Ghisoni, J. Chiffelle, A. Orcurto, J. Dagher, N. Fahr, F. Benedetti, I. Crespo, S. Zimmermann, R. Duran, M. Imbimbo, M. Ochoa De Olza, B. Navarro, K. Homiscko, S. Bobisse, , G. Coukos

This study identifies CD8+ TIL-myeloid cell networks as predictors of response to adoptive TIL therapy in melanoma using single-cell RNA-seq and spatial proteomic analyses.

bioRxiv · Dec 23, 2022 · PreprintImmunology

Sentinel2GlobalLULC: A deep-learning-ready Sentinel-2 RGB image dataset for global land use/cover mapping

Y. Benhammou, D. Alcaraz-Segura, E. Guirado, R. Khaldi, B. Achchab, F. Herrera, S. Tabik

The paper introduces a Sentinel-2 RGB image dataset for global land use/cover mapping, optimized for deep learning to improve consistency and accuracy in LULC classification.

bioRxiv · Dec 1, 2021 · PreprintEcology

Asymmetric post-translational modifications regulate the intracellular distribution of unstimulated STAT3 dimers

B. Cardoso, R. Letra-Vilela, C. Silva-Almeida, A. M. Rocha, F. Murtinheira, C. Rodriguez, V. Martin, F. Herrera

The paper shows asymmetric post-translational modifications alter the intracellular distribution of STAT3 dimers, revealing a new regulatory mechanism.

bioRxiv · Feb 22, 2019 · PreprintCell Biology

Automatic whale counting in satellite images with deep learning

E. Guirado, S. Tabik, M. L. Rivas, D. Alcaraz-Segura, F. Herrera

This paper develops a deep learning system for automatically counting whales from satellite images, achieving 84% detection and 97% counting accuracy in global whale-watching hotspots.

bioRxiv · Oct 16, 2018Ecology