Author

Alejandro Cabrera‐Andrade

Universidade da CoruñaORCID 0000-0001-9702-6618Unclaimed 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:drug response / drug discovery· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Assay:protein sequence· 2 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:protein design· 1

Frequent Co-Authors

Alejandro PazosiD· 3 shared Andrés López‐CortésiD· 3 shared César Paz‐y‐MiñoiD· 3 shared Cristian Robert MunteanuiD· 3 shared Eduardo TejeraiD· 3 shared Humberto González‐DíaziD· 3 shared Yunierkis Pérez‐CastilloiD· 3 shared Santiago Xavier GuerreroiD· 2 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 43 mutual co-author links

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3 shared papersBioinformatics

Alejandro PazosORCID

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3 papers

Recent·Most cited

GenomicsScientific ReportsPeer-reviewedMar 24, 2020

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

Andrés López‐Cortés, César Paz‐y‐Miño, Santiago Xavier Guerrero…·Eduardo Tejera

This study identifies essential genes in breast cancer using OncoOmics approaches, revealing 144 genes with 33% being cancer hallmarks and 14% significant across multiple methods.

52 citations

humanbulk RNA-seq

𝕏 PostBluesky

BioinformaticsbioRxivPreprintNov 13, 2019

Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

Andrés López‐Cortés, Alejandro Cabrera‐Andrade, José M. Vázquez-Naya…·Cristian Robert Munteanu

The paper develops an artificial neural network model to predict breast cancer-related proteins, achieving high accuracy and identifying top-ranked proteins in three categories.

3 citations

humanprotein sequence

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BioinformaticsbioRxivPreprintOct 31, 2019

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

Andrés López‐Cortés, Alejandro Cabrera‐Andrade, Carlos Manuel Cruz Segundo…·Cristian Robert Munteanu

The paper develops a machine learning model to predict druggable proteins, achieving high accuracy, and identifies potential targets in cancer and RNA-binding proteins.

12 citations

protein sequencemachine learning (classical)

𝕏 PostBluesky