Author

A. Zapater-Moros

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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Cancer Biology· 4

Signature Assays, Organisms & Methods

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Organism:human· 4 Approach:statistical model· 4 Research Focus:differential expression· 3 Assay:proteomics· 3 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:gene regulatory network inference· 1 Research Focus:variant calling· 1

Frequent Co-Authors

A. Gamez-Pozo· 4 shared G. Prado-Vazquez· 4 shared L. Trilla-Fuertes· 4 shared R. Lopez-Vacas· 4 shared E. Espinosa· 3 shared J. A. Fresno Vara· 3 shared E. Lopez-Camacho· 2 shared M. Diaz-Almiron· 2 shared

Activity by Year

2018-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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4 shared papersCancer Biology

A. Gamez-Pozo

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4 papers

Recent·Most cited

Molecular characterization of triple negative breast cancer formaldehyde-fixed paraffin-embedded samples by data independent acquisition proteomics

S. Garcia-Adrian, L. Trilla-Fuertes, A. Gamez-Pozo, C. Chiva, R. Lopez-Vacas, E. Lopez-Camacho, G. Prado-Vazquez, A. Zapater-Moros, M. I. Lumbreras-Herrera, D. Hardisson, L. Yebenes, P. Zamora, E. Sabido, J. A. Fresno Vara, E. Espinosa

This study uses proteomics to identify molecular subgroups and a prognostic signature in triple negative breast cancer samples, revealing differences in biological processes and potential therapeutic targets.

bioRxiv · Sep 21, 2020Cancer Biology

Comprehensive characterization of the mutational landscape in localized anal squamous cell carcinoma

L. Trilla-Fuertes, I. Ghanem, J. Maurel, L. G-Pastrian, M. Mendiola, C. Pena, R. Lopez-Vacas, G. Prado-Vazquez, E. Lopez-Camacho, A. Zapater-Moros, V. Heredia, M. Cuatrecasas, P. Garcia-Alfonso, J. Capdevila, C. Conill, , J. A. Fresno

This study identifies high-impact genetic variants in anal squamous cell carcinoma and links some to poor prognosis.

bioRxiv · Oct 7, 2019Cancer Biology

Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

L. Trilla-Fuertes, A. Gamez-Pozo, M. Diaz-Almiron, G. Prado-Vazquez, A. Zapater-Moros, R. Lopez-Vacas, P. Nanni, P. Zamora, E. Espinosa, J. A. Fresno Vara

The paper uses flux balance analysis on proteomics data to identify metabolic pathways associated with prognosis in breast cancer patients.

bioRxiv · Nov 12, 2018Cancer Biology

Bayesian Networks established functional differences between breast cancer subtypes

L. Trilla-Fuertes, A. Zapater-Moros, A. Gamez-Pozo, J. M. Arevalillo, G. Prado-Vazquez, M. Diaz-Almiron, M. Ferrer-Gomez, R. Lopez-Vacas, H. Navarro, E. Espinosa, P. Main, J. A. Fresno Vara

The paper uses Bayesian networks and proteomics data to identify functional differences between breast cancer subtypes, including a new TN-like subtype, and finds a metastasis-related component with prognostic value.

bioRxiv · May 18, 2018Cancer Biology