María Rodríguez Martínez

Orcid: 0000-0003-3766-4233

According to our database1, María Rodríguez Martínez authored at least 32 papers between 2016 and 2024.

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Bibliography

2024
Conformal Autoregressive Generation: Beam Search with Coverage Guarantees.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
FLAN: feature-wise latent additive neural models for biological applications.
Briefings Bioinform., May, 2023

DockGame: Cooperative Games for Multimeric Rigid Protein Docking.
CoRR, 2023

Adaptive Conformal Regression with Jackknife+ Rescaled Scores.
CoRR, 2023

Aligned Diffusion Schrödinger Bridges.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

2022
Computational modelling in health and disease: highlights of the 6th annual SysMod meeting.
Bioinform., October, 2022

PCfun: a hybrid computational framework for systematic characterization of protein complex function.
Briefings Bioinform., 2022

Is Attention Interpretation? A Quantitative Assessment on Sets.
Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022

Attention-Based Interpretable Regression of Gene Expression in Histology.
Proceedings of the Interpretability of Machine Intelligence in Medical Image Computing, 2022

2021
The Multiple Dimensions of Networks in Cancer: A Perspective.
Symmetry, 2021

Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2.
Mach. Learn. Sci. Technol., 2021

It's FLAN time! Summing feature-wise latent representations for interpretability.
CoRR, 2021

TITAN: T-cell receptor specificity prediction with bimodal attention networks.
Bioinform., 2021

SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems.
Bioinform., 2021

On the feasibility of deep learning applications using raw mass spectrometry data.
Bioinform., 2021

2020
FPGA Accelerated Analysis of Boolean Gene Regulatory Networks.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020

PaccMann: a web service for interpretable anticancer compound sensitivity prediction.
Nucleic Acids Res., 2020

Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks.
CoRR, 2020

Learning Invariances for Interpretability using Supervised VAE.
CoRR, 2020

On quantitative aspects of model interpretability.
CoRR, 2020

PaccMann<sup>RL</sup> on SARS-CoV-2: Designing antiviral candidates with conditional generative models.
CoRR, 2020

PaccMann<sup>RL</sup>: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning.
Proceedings of the Research in Computational Molecular Biology, 2020

2019
Context-specific interaction networks from vector representation of words.
Nat. Mach. Intell., 2019

DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data.
CoRR, 2019

MonoNet: Towards Interpretable Models by Learning Monotonic Features.
CoRR, 2019

Reinforcement learning-driven de-novo design of anticancer compounds conditioned on biomolecular profiles.
CoRR, 2019

Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders.
CoRR, 2019

edGNN: a Simple and Powerful GNN for Directed Labeled Graphs.
CoRR, 2019

2018
Inference of the three-dimensional chromatin structure and its temporal behavior.
CoRR, 2018

PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks.
CoRR, 2018

Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer.
CoRR, 2018

2016
Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016


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