Search Results for author:
Found 20 papers, 10 papers with code
Atom-Level Optical Chemical Structure Recognition with Limited Supervision
Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development.
How good Neural Networks interpretation methods really are? A quantitative benchmark
For this purpose, we built synthetic datasets with nonlinearly separable classes and increasing number of decoy (random) features, illustrating the challenge of FS in high-dimensional settings.
Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection
In this work, we propose ProbKT, a framework based on probabilistic logical reasoning that allows to train object detection models with arbitrary types of weak supervision.
Industry-Scale Orchestrated Federated Learning for Drug Discovery
To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups.
SparseChem: Fast and accurate machine learning model for small molecules
SparseChem provides fast and accurate machine learning models for biochemical applications.
Self-Labeling of Fully Mediating Representations by Graph Alignment
In this paper we investigate the scenario of domain adaptation from the source domain where we have access to the expensive labels $V$ to the target domain where only normal labels W are available.
Topological Graph Neural Networks
Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles.
Latent Convergent Cross Mapping
Discovering causal structures of temporal processes is a major tool of scientific inquiry because it helps us better understand and explain the mechanisms driving a phenomenon of interest, thereby facilitating analysis, reasoning, and synthesis for such systems.
Longitudinal modeling of MS patient trajectories improves predictions of disability progression
In this work, we address the task of optimally extracting information from longitudinal patient data in the real-world setting with a special focus on the sporadic sampling problem.
Multilevel Gibbs Sampling for Bayesian Regression
Additionally, the use of correlated samples is investigated for variance reduction to improve the convergence of the Markov Chain.
ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning
Many thousands of scientific articles in chemistry and pharmaceutical sciences have investigated chemical compounds, but in cases the details of the structure of these chemical compounds is published only as an images.
Expressive Graph Informer Networks
Applying machine learning to molecules is challenging because of their natural representation as graphs rather than vectors. Several architectures have been recently proposed for deep learning from molecular graphs, but they suffer from informationbottlenecks because they only pass information from a graph node to its direct neighbors.
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i. e., sampling is irregular both in time and across dimensions)-such as in the case of clinical patient data.
Ranked #2 on
Multivariate Time Series Forecasting
on MIMIC-III
SMURFF: a High-Performance Framework for Matrix Factorization
Bayesian Matrix Factorization (BMF) is a powerful technique for recommender systems because it produces good results and is relatively robust against overfitting.
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification
We present a generative approach to classify scarcely observed longitudinal patient trajectories.
Fast semi-supervised discriminant analysis for binary classification of large data-sets
High-dimensional data requires scalable algorithms.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
Highly Scalable Tensor Factorization for Prediction of Drug-Protein Interaction Type
Method: to analyze the interaction types we propose factorization method Macau which allows us to combine different measurement types into a single tensor together with proteins and compounds.
Macau: Scalable Bayesian Multi-relational Factorization with Side Information using MCMC
We propose Macau, a powerful and flexible Bayesian factorization method for heterogeneous data.
Easy Hyperparameter Search Using Optunity
Optunity is a free software package dedicated to hyperparameter optimization.
