Search Results for author: María Rodríguez Martínez

Found 14 papers, 5 papers with code

Attention-based Interpretable Regression of Gene Expression in Histology

1 code implementation29 Aug 2022 Mara Graziani, Niccolò Marini, Nicolas Deutschmann, Nikita Janakarajan, Henning Müller, María Rodríguez Martínez

Interpretability of deep learning is widely used to evaluate the reliability of medical imaging models and reduce the risks of inaccurate patient recommendations.


TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks

no code implementations21 Apr 2021 Anna Weber, Jannis Born, María Rodríguez Martínez

Scarcity of data and a large sequence space make this task challenging, and to date only models limited to a small set of epitopes have achieved good performance.

Data Augmentation Specificity +1

On the Importance of Looking at the Manifold

no code implementations1 Jan 2021 Nil Adell Mill, Jannis Born, Nathaniel Park, James Hedrick, María Rodríguez Martínez, Matteo Manica

We explore a spectrum of models, ranging from uniquely learning representations based on the isolated features of the nodes (focusing on Variational Autoencoders), to uniquely learning representations based on the topology (using node2vec) passing through models that integrate both node features and topological information in a hybrid fashion.

Representation Learning

Learning Invariances for Interpretability using Supervised VAE

no code implementations15 Jul 2020 An-phi Nguyen, María Rodríguez Martínez

If we understand a problem, we may introduce inductive biases in our model in the form of invariances.

On quantitative aspects of model interpretability

no code implementations15 Jul 2020 An-phi Nguyen, María Rodríguez Martínez

Despite the growing body of work in interpretable machine learning, it remains unclear how to evaluate different explainability methods without resorting to qualitative assessment and user-studies.

Interpretable Machine Learning

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

1 code implementation27 May 2020 Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, María Rodríguez Martínez

With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents.

Drug Discovery

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

no code implementations18 Nov 2019 Ali Oskooei, Sophie Mai Chau, Jonas Weiss, Arvind Sridhar, María Rodríguez Martínez, Bruno Michel

We explore and compare three methods in order to perform unsupervised stress detection: 1) traditional K-Means clustering with engineered time and frequency domain features 2) convolutional autoencoders and 3) long short-term memory (LSTM) autoencoders, both trained on the raw RRI measurements combined with DBSCAN clustering and K-Nearest-Neighbors classification.

Heart Rate Variability Time Series Analysis

MonoNet: Towards Interpretable Models by Learning Monotonic Features

no code implementations30 Sep 2019 An-phi Nguyen, María Rodríguez Martínez

Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance.

BIG-bench Machine Learning Interpretable Machine Learning

PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning

no code implementations29 Aug 2019 Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, Karsten Borgwardt, María Rodríguez Martínez

The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i. e., transcriptomic profile).

reinforcement-learning Reinforcement Learning (RL)

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

1 code implementation25 Apr 2019 Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder.

edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

1 code implementation18 Apr 2019 Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez, Jean-Philippe Thiran, Maria Gabrani

The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings.

Graph Classification

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

no code implementations22 Nov 2018 Bianca-Cristina Cristescu, Zalán Borsos, John Lygeros, María Rodríguez Martínez, Maria Anna Rapsomaniki

In this work, we explore the idea of manifold learning for the 3D chromatin structure inference and present a novel method, REcurrent Autoencoders for CHromatin 3D structure prediction (REACH-3D).

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

1 code implementation16 Nov 2018 Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

Our models ingest a drug-cell pair consisting of SMILES encoding of a compound and the gene expression profile of a cancer cell and predicts an IC50 sensitivity value.

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