Search Results for author:
Found 29 papers, 13 papers with code
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders
Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a non-destructive, label-free manner.
A continuous Structural Intervention Distance to compare Causal Graphs
Understanding and adequately assessing the difference between a true and a learnt causal graphs is crucial for causal inference under interventions.
Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions
Models that rely solely on pairwise relationships often fail to capture the complete statistical structure of the complex multivariate data found in diverse domains, such as socio-economic, ecological, or biomedical systems.
Kernel-based Joint Independence Tests for Multivariate Stationary and Non-stationary Time Series
Multivariate time series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas.
Persistent Homology of the Multiscale Clustering Filtration
In many applications in data clustering, it is desirable to find not just a single partition into clusters but a sequence of partitions describing the data at different scales, or levels of coarseness.
Interpretable statistical representations of neural population dynamics and geometry
The dynamics of neuron populations during many behavioural tasks evolve on low-dimensional manifolds.
Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions
This paper is a work in progress.
Deep incremental learning models for financial temporal tabular datasets with distribution shifts
We present a robust deep incremental learning framework for regression tasks on financial temporal tabular datasets which is built upon the incremental use of commonly available tabular and time series prediction models to adapt to distributional shifts typical of financial datasets.
Online learning techniques for prediction of temporal tabular datasets with regime changes
The application of deep learning to non-stationary temporal datasets can lead to overfitted models that underperform under regime changes.
Prediction of protein allosteric signalling pathways and functional residues through paths of optimised propensity
Key residues in both orthosteric and allosteric sites were identified and showed agreement with experimental results, and pivotal signalling residues along the pathway were also revealed, thus providing alternative targets for drug design.
ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations
Early diagnosis of disease can lead to improved health outcomes, including higher survival rates and lower treatment costs.
Similarity measure for sparse time course data based on Gaussian processes
We propose a similarity measure for sparsely sampled time course data in the form of a log-likelihood ratio of Gaussian processes (GP).
Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles
Production of news content is growing at an astonishing rate.
Computation of single-cell metabolite distributions using mixture models
The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways.
Opportunities at the interface of network science and metabolic modelling
Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science.
Severability of mesoscale components and local time scales in dynamical networks
A major goal of dynamical systems theory is the search for simplified descriptions of the dynamics of a large number of interacting states.
BART-based inference for Poisson processes
The effectiveness of Bayesian Additive Regression Trees (BART) has been demonstrated in a variety of contexts including non-parametric regression and classification.
Geometric graphs from data to aid classification tasks with graph convolutional networks
Traditional classification tasks learn to assign samples to given classes based solely on sample features.
HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways
The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging.
Quantitative Methods Genomics Methodology
Semi-supervised classification on graphs using explicit diffusion dynamics
Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples.
Graph-based data clustering via multiscale community detection
We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework.
Extracting information from free text through unsupervised graph-based clustering: an application to patient incident records
The large volume of text in electronic healthcare records often remains underused due to a lack of methodologies to extract interpretable content.
Learning spatiotemporal signals using a recurrent spiking network that discretizes time
Learning to produce spatiotemporal sequences is a common task that the brain has to solve.
Quantifying the Alignment of Graph and Features in Deep Learning
We showcase the relationship between the SAM and the classification performance through the study of limiting cases of GCNs and systematic randomizations of both features and graph structure applied to a constructive example and several examples of citation networks of different origins.
From Free Text to Clusters of Content in Health Records: An Unsupervised Graph Partitioning Approach
Electronic Healthcare records contain large volumes of unstructured data in different forms.
Content-driven, unsupervised clustering of news articles through multiscale graph partitioning
The explosion in the amount of news and journalistic content being generated across the globe, coupled with extended and instantaneous access to information through online media, makes it difficult and time-consuming to monitor news developments and opinion formation in real time.
From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology
Electronic Healthcare Records contain large volumes of unstructured data, including extensive free text.
Multiscale dynamical embeddings of complex networks
Complex systems and relational data are often abstracted as dynamical processes on networks.
Social and Information Networks Systems and Control Physics and Society
Toggling a Genetic Switch Using Reinforcement Learning
In this paper, we consider the problem of optimal exogenous control of gene regulatory networks.
