Author

Pilar Ballesteros-Cuartero

Every paper listing this name carries an ORCID.

Identified by ORCID

Pilar Ballesteros-CuarteroiD3 papers · Consejo Superior de Investigaciones Científicas

Biology Disciplines

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Bioinformatics· 1 Cancer Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Almudena Méndez‐PéreziD· 1 shared Andreas N. WICKIiD· 1 shared Andrés M. Acosta-MorenoiD· 1 shared Arrate Muñoz‐BarrutiaiD· 1 shared Berend SnijderiD· 1 shared Carlos Óscar S. SorzanoiD· 1 shared Carlos Wert-CarvajaliD· 1 shared Cinzia EspositoiD· 1 shared

Activity by Year

2023-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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Curved chords = mutual co-authorship

1 shared paperImmunology

Almudena Méndez‐PérezORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Cancer BiologybioRxivPreprintOct 13, 2026

An RFX-driven ciliogenic cell state defines ex vivo FGFR-inhibitor sensitivity in colorectal cancer

Lucie Kralickova, Nicola Miglino, Cinzia Esposito…·Berend Snijder

The paper identifies an RFX-driven ciliogenic cell state in colorectal cancer that is sensitive to FGFR inhibitors and associated with poor prognosis.

DOI 10.64898/2026.10.12.756637

humanbulk RNA-seq

𝕏 PostBluesky

BioinformaticsbioRxivPreprintAug 28, 2026

Label Noise Limits TCR-pMHC Specificity Prediction: Improved Performance Through AlphaFold3-Based Structural Modeling and Data Denoising

Pilar Ballesteros-Cuartero, Johanne Lund, Morten Nielsen

The paper shows label noise limits TCR-pMHC specificity prediction and improves performance using AlphaFold3 and denoising.

DOI 10.64898/2026.08.25.746957

protein structuredeep learning

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ImmunologybioRxivNov 22, 2023

Unraveling the Power of NAP-CNB's Machine Learning-enhanced Tumor Neoantigen Prediction

Almudena Méndez‐Pérez, Andrés M. Acosta-Moreno, Carlos Wert-Carvajal…·Esteban Veiga

This study validates a machine learning platform, NAP-CNB, for identifying tumor neoantigens using RNAseq data, showing improved antitumor responses with multiantigen vaccination.

2 citations

humanbulk RNA-seq

𝕏 PostBluesky