Marina Esteban-Medina

Postdoc, Computer Science, ETHZ - ETH Zurich

Joined

October 2025

Names

Marina Esteban-Medina (Preferred)

Emails

****@inf.ethz.ch (Confirmed)

****@ai.ethz.ch (Confirmed)

Personal Links

Homepage

Google Scholar

Career & Education History

Postdoc

Computer Science, ETHZ - ETH Zurich (ethz.ch)

2024 – 2026

PhD student

Department of Computer Science, University of Massachusetts at Amherst (cs.umass.edu)

2020 – 2024

Advisors, Relations & Conflicts

No relations added

Expertise

computational Biology

Present

Deep learning for healhcare

Present

Domain shift adaptations

Present

Publications

Where Simple Baselines Fail: Mapping the Modeling Frontier of Perturbation Prediction
Anna Kalygina, Alexander Theus, Marina Esteban-Medina, Valentina Boeva
- GenBio 2026 Poster
- Readers: Everyone
Domain-adaptation deep learning models do not outperform simple baseline models in single-cell anti-cancer drug sensitivity prediction
Michael Bohl, Marina Esteban-Medina, Niko Beerenwinkel, Kerstin Lenhof
- Crossref
- Readers: Everyone
Data Associated with "Can deep learning models for drug sensitivity prediction truly transfer knowledge from bulk to single cell data?"
Michael Bohl, Marina Esteban-Medina, Kerstin Lenhof
- Zenodo
- Readers: Everyone
COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.
Marek Ostaszewski, Anna Niarakis, Alexander Mazein, Inna Kuperstein, Robert Phair, Aurelio Orta-Resendiz, Vidisha Singh, Sara Sadat Aghamiri, Marcio Luis Acencio, Enrico Glaab, Andreas Ruepp, Gisela Fobo, Corinna Montrone, Barbara Brauner, Goar Frishman, Luis Cristóbal Monraz Gómez, Julia Somers, Matti Hoch, Shailendra Kumar Gupta, Julia Scheel et al. (120 additional authors not shown)
- Springer Nature
- Readers: Everyone
The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery
Marina Esteban-Medina, Carlos Loucera, Kinza Rian, Sheyla Velasco, Lorena Olivares-González, Regina Rodrigo, Joaquin Dopazo, Maria Peña-Chilet
- figshare
- Readers: Everyone
Additional file 1 of Real-world evidence with a retrospective cohort of 15,968 COVID-19 hospitalized patients suggests 21 new effective treatments
Carlos Loucera, Rosario Carmona, Marina Esteban-Medina, Gerrit Bostelmann, Dolores Muñoyerro-Muñiz, Román Villegas, María Peña-Chilet, Joaquín Dopazo
- figshare
- Readers: Everyone
Real-world evidence with a retrospective cohort of 15,968 COVID-19 hospitalized patients suggests 21 new effective treatments
Carlos Loucera, Rosario Carmona, Marina Esteban-Medina, Gerrit Bostelmann, Dolores Muñoyerro-Muñiz, Román Villegas, María Peña-Chilet, Joaquín Dopazo
- figshare
- Readers: Everyone

View all 30 publications

Co-Authors

View all 170 co-authors

Marina Esteban-Medina

Names

Emails

Personal Links

Career & Education History

Advisors, Relations & Conflicts

Expertise

Publications

Where Simple Baselines Fail: Mapping the Modeling Frontier of Perturbation Prediction

Domain-adaptation deep learning models do not outperform simple baseline models in single-cell anti-cancer drug sensitivity prediction

Data Associated with "Can deep learning models for drug sensitivity prediction truly transfer knowledge from bulk to single cell data?"

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery

Additional file 1 of Real-world evidence with a retrospective cohort of 15,968 COVID-19 hospitalized patients suggests 21 new effective treatments

Real-world evidence with a retrospective cohort of 15,968 COVID-19 hospitalized patients suggests 21 new effective treatments

Co-Authors