16 Result(s)
within
Nataliya Sokolovska
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Article
Learning preference representations based on Choquet integrals for multicriteria decision making
This paper concerns preference elicitation and learning of decision models in the context of multicriteria decision making. We propose an approach to learn a representation of preferences by a non-additive mul...
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Article
Open AccessLatent dirichlet allocation for double clustering (LDA-DC): discovering patients phenotypes and cell populations within a single Bayesian framework
Current clinical routines rely more and more on “omics” data such as flow cytometry data from host and microbiota. Cohorts variability in addition to patients’ heterogeneity and huge dimensions make it difficu...
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Article
Combinatorial, additive and dose-dependent drug–microbiome associations
During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker ...
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Article
Open AccessFunctional prediction of environmental variables using metabolic networks
In this manuscript, we propose a novel approach to assess relationships between environment and metabolic networks. We used a comprehensive dataset of more than 5000 prokaryotic species from which we derived t...
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Chapter and Conference Paper
A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks
Graph neural networks (GNNs) have known an increasing success recently, with many GNN variants achieving state-of-the-art results on node and graph classification tasks. The proposed GNNs, however, often imple...
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Article
Open AccessRobust structure measures of metabolic networks that predict prokaryotic optimal growth temperature
Metabolic networks reflect the relationships between metabolites (biomolecules) and the enzymes (proteins), and are of particular interest since they describe all chemical reactions of an organism. The metabol...
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Chapter and Conference Paper
Risk Scores Learned by Deep Restricted Boltzmann Machines with Trained Interval Quantization
A compact easily applicable and highly accurate classification model is of a big interest in decision making. A simple scoring system which stratifies patients efficiently can help a clinician in diagnostics o...
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Chapter and Conference Paper
A Semi-supervised Approach to Discover Bivariate Causality in Large Biological Data
An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational dat...
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Article
The advanced-DiaRem score improves prediction of diabetes remission 1 year post-Roux-en-Y gastric bypass
Not all people with type 2 diabetes who undergo bariatric surgery achieve diabetes remission. Thus it is critical to develop methods for predicting outcomes that are applicable for clinical practice. The DiaRe...
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Article
Open AccessSpectral consensus strategy for accurate reconstruction of large biological networks
The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to lar...
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Chapter and Conference Paper
Continuous and Discrete Deep Classifiers for Data Integration
Data representation in a lower dimension is needed in applications, where information comes from multiple high dimensional sources. A final compact model has to be interpreted by human experts, and interpretat...
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Chapter and Conference Paper
Sparse Gradient-Based Direct Policy Search
Reinforcement learning is challenging if state and action spaces are continuous. The discretization of state and action spaces and real-time adaptation of the discretization are critical issues in reinforcemen...
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Chapter and Conference Paper
Q-Learning with Double Progressive Widening: Application to Robotics
Discretization of state and action spaces is a critical issue in Q-Learning. In our contribution, we propose a real-time adaptation of the discretization by the progressive widening technique which has been alrea...
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Chapter and Conference Paper
A Principled Method for Exploiting Opening Books
In the past we used a great deal of computational power and human expertise for storing a rather big dataset of good 9x9 Go games, in order to build an opening book. We improved the algorithm used for generati...
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Chapter and Conference Paper
Continuous Upper Confidence Trees
Upper Confidence Trees are a very efficient tool for solving Markov Decision Processes; originating in difficult games like the game of Go, it is in particular surprisingly efficient in high dimensional proble...
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Chapter and Conference Paper
Aspects of Semi-supervised and Active Learning in Conditional Random Fields
Conditional random fields are among the state-of-the art approaches to structured output prediction, and the model has been adopted for various real-world problems. The supervised classification is expensive, ...
