163 citations · 240 across the 11 of their papers we have counts for
11 papers
Aligned Diffusion Schrödinger Bridges
Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh +3
Diffusion Schrödinger bridges (DSB) have recently emerged as a powerful framework for recovering stochastic dynamics via their marginal observations at different time points. Despi…
It's FLAN time! Summing feature-wise latent representations for interpretability
An-phi Nguyen, Maria Rodriguez Martinez
Interpretability has become a necessary feature for machine learning models deployed in critical scenarios, e.g. legal system, healthcare. In these situations, algorithmic decision…
Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks
Modestas Filipavicius, Matteo Manica, Joris Cadow +1
Less than 1% of protein sequences are structurally and functionally annotated. Natural Language Processing (NLP) community has recently embraced self-supervised learning as a power…
MonoNet: Towards Interpretable Models by Learning Monotonic Features
An-phi Nguyen, María Rodríguez Martínez
Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance. This is especially true when there is interest in deploying data…
PaccMann: Designing anticancer drugs from transcriptomic data via reinforcement learning
Jannis Born, Matteo Manica, Ali Oskooei +3
With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep le…
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
Matteo Manica, Ali Oskooei, Jannis Born +3
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…
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez +2
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. Building on pr…
Inference of the three-dimensional chromatin structure and its temporal behavior
Bianca-Cristina Cristescu, Zalán Borsos, John Lygeros +2
Understanding the three-dimensional (3D) structure of the genome is essential for elucidating vital biological processes and their links to human disease. To determine how the geno…
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
Ali Oskooei, Jannis Born, Matteo Manica +3
We present a novel approach for the prediction of anticancer compound sensitivity by means of multi-modal attention-based neural networks (PaccMann). In our approach, we integrate…
Free sequences in P(ω)/fin
David Chodounský, Vera Fischer, Jan Grebík
We investigate maximal free sequences in the Boolean algebra , as defined by D. Monk. We provide some information on the general structure of these obj…
High-throughput validation of ceRNA regulatory networks
Hua-Sheng Chiu, María Rodríguez Martínez, Mukesh Bansal +5
Background: MicroRNAs (miRNAs) play multiple roles in tumor biology [1]. Interestingly, reports from multiple groups suggest that miRNA targets may be coupled through competitive s…