María Rodríguez Martínez

According to our database, María Rodríguez Martínez authored at least 34 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Adaptive Conformal Regression with Split-Jackknife+ Scores.

Nicolas Deutschmann, Mattia Rigotti, María Rodríguez Martínez

Trans. Mach. Learn. Res., 2024

Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases.

Ling Han, Hao Huang, Dustin Scheinost, Mary-Anne Hartley, María Rodríguez Martínez

CoRR, 2024

Conformal Autoregressive Generation: Beam Search with Coverage Guarantees.

Nicolas Deutschmann, Marvin Alberts, María Rodríguez Martínez

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

FLAN: feature-wise latent additive neural models for biological applications.

An-phi Nguyen, Stefania Vasilaki, María Rodríguez Martínez

Briefings Bioinform., May, 2023

DockGame: Cooperative Games for Multimeric Rigid Protein Docking.

Vignesh Ram Somnath, Pier Giuseppe Sessa, María Rodríguez Martínez, Andreas Krause

CoRR, 2023

Adaptive Conformal Regression with Jackknife+ Rescaled Scores.

Nicolas Deutschmann, Mattia Rigotti, María Rodríguez Martínez

CoRR, 2023

Aligned Diffusion Schrödinger Bridges.

Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, María Rodríguez Martínez, Andreas Krause, Charlotte Bunne

Proceedings of the Uncertainty in Artificial Intelligence, 2023

2022

Computational modelling in health and disease: highlights of the 6th annual SysMod meeting.

Anna Niarakis, Juilee Thakar, Matteo Barberis, María Rodríguez Martínez, Tomás Helikar, Marc R. Birtwistle, Claudine Chaouiya, Laurence Calzone, Andreas Dräger

Bioinform., October, 2022

PCfun: a hybrid computational framework for systematic characterization of protein complex function.

Varun S. Sharma, Andrea Fossati, Rodolfo Ciuffa, Marija Buljan, Evan G. Williams, Zhen Chen, Wenguang Shao, Patrick G. A. Pedrioli, Anthony W. Purcell, María Rodríguez Martínez, Jiangning Song, Matteo Manica, Ruedi Aebersold, Chen Li

Briefings Bioinform., 2022

Is Attention Interpretation? A Quantitative Assessment on Sets.

Jonathan Haab, Nicolas Deutschmann, María Rodríguez Martínez

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022

Attention-Based Interpretable Regression of Gene Expression in Histology.

Mara Graziani, Niccolò Marini, Nicolas Deutschmann, Nikita Janakarajan, Henning Müller, María Rodríguez Martínez

Proceedings of the Interpretability of Machine Intelligence in Medical Image Computing, 2022

2021

The Multiple Dimensions of Networks in Cancer: A Perspective.

Cristian Axenie, Roman Bauer, María Rodríguez Martínez

Symmetry, 2021

Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2.

Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, Nikita Janakarajan, Antonio Cardinale, Teodoro Laino, María Rodríguez Martínez

Mach. Learn. Sci. Technol., 2021

It's FLAN time! Summing feature-wise latent representations for interpretability.

An-phi Nguyen, María Rodríguez Martínez

CoRR, 2021

TITAN: T-cell receptor specificity prediction with bimodal attention networks.

Anna Weber, Jannis Born, María Rodríguez Martínez

Bioinform., 2021

SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems.

Andreas Dräger, Tomás Helikar, Matteo Barberis, Marc R. Birtwistle, Laurence Calzone, Claudine Chaouiya, Jan Hasenauer, Jonathan R. Karr, Anna Niarakis, María Rodríguez Martínez, Julio Saez-Rodriguez, Juilee Thakar

Bioinform., 2021

On the feasibility of deep learning applications using raw mass spectrometry data.

Joris Cadow, Matteo Manica, Roland Mathis, Tiannan Guo, Ruedi Aebersold, María Rodríguez Martínez

Bioinform., 2021

2020

FPGA Accelerated Analysis of Boolean Gene Regulatory Networks.

Matteo Manica, Raphael Polig, Mitra Purandare, Roland Mathis, Christoph Hagleitner, María Rodríguez Martínez

IEEE ACM Trans. Comput. Biol. Bioinform., 2020

PaccMann: a web service for interpretable anticancer compound sensitivity prediction.

Joris Cadow, Jannis Born, Matteo Manica, Ali Oskooei, María Rodríguez Martínez

Nucleic Acids Res., 2020

Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks.

Modestas Filipavicius, Matteo Manica, Joris Cadow, María Rodríguez Martínez

CoRR, 2020

Learning Invariances for Interpretability using Supervised VAE.

An-phi Nguyen, María Rodríguez Martínez

CoRR, 2020

On quantitative aspects of model interpretability.

An-phi Nguyen, María Rodríguez Martínez

CoRR, 2020

PaccMann^RL on SARS-CoV-2: Designing antiviral candidates with conditional generative models.

Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, María Rodríguez Martínez

CoRR, 2020

PaccMann^RL: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning.

Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, María Rodríguez Martínez

Proceedings of the Research in Computational Molecular Biology, 2020

2019

Context-specific interaction networks from vector representation of words.

Matteo Manica, Roland Mathis, Joris Cadow, María Rodríguez Martínez

Nat. Mach. Intell., 2019

DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data.

Ali Oskooei, Sophie Mai Chau, Jonas R. M. Weiss, Arvind Sridhar, María Rodríguez Martínez, Bruno Michel

CoRR, 2019

MonoNet: Towards Interpretable Models by Learning Monotonic Features.

An-phi Nguyen, María Rodríguez Martínez

CoRR, 2019

Reinforcement learning-driven de-novo design of anticancer compounds conditioned on biomolecular profiles.

Jannis Born, Matteo Manica, Ali Oskooei, María Rodríguez Martínez

CoRR, 2019

Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders.

Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

CoRR, 2019

edGNN: a Simple and Powerful GNN for Directed Labeled Graphs.

Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez, Jean-Philippe Thiran, Maria Gabrani

CoRR, 2019

2018

Inference of the three-dimensional chromatin structure and its temporal behavior.

Bianca-Cristina Cristescu, Zalán Borsos, John Lygeros, María Rodríguez Martínez, Maria Anna Rapsomaniki

CoRR, 2018

PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks.

Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

CoRR, 2018

Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer.

Ali Oskooei, Matteo Manica, Roland Mathis, María Rodríguez Martínez

CoRR, 2018

2016

Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations.

Lukas Studer, Loïc Paulevé, Christoph Zechner, Matthias Reumann, María Rodríguez Martínez, Heinz Koeppl

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016