barahona-research-group Goto Github PK

bayesfactorsimilarity

Code for the paper "Similarity Measure for Sparse Time Course Data Based on Gaussian Processes" by Z Liu and M Barahona, accepted at UAI 2021, https://arxiv.org/abs/2102.12342

chemotaxis-in-rugged-landscapes

Code for the paper: Gosztolai, A., Barahona, M. "Cellular memory enhances bacterial chemotactic navigation in rugged environments". Commun Phys 3, 47 (2020). https://doi.org/10.1038/s42005-020-0312-8 . The code allows the simulation of bacterial chemotaxis based on run-and-tumble motion in rugged chemoattractant landscapes.

dhsic_ts

Higher order interactions python package

dyngdim

Dynamic Graph Dimensionality is a methodology for computing the relative, local and global dimension of complex networks.

gdr

Graph Diffusion Reclassification - Code from the paper "Semi-supervised classification on graphs using explicit diffusion dynamics" by RL Peach, A Arnaudon and M Barahona, Foundations of Data Science 2 (1), 19-33 (2020)

graphbasedclustering

Multiresolution clustering of data using geometric graphs --- Code from "Graph-based data clustering via multiscale community detection" by Z Liu and M Barahona, Applied Network Science, 5 (3) (2020). See also: https://wwwf.imperial.ac.uk/~mpbara/Partition_Stability/

hcga

Highly Comparative Graph Analysis - Code for network phenotyping

ice-node

Integration of Clinical Embeddings with Neural ODEs

lgde

Python code for the paper "LGDE: Local Graph-based Dictionary Expansion" by Dominik J Schindler, Sneha Jha, Xixuan Zhang, Kilian Buehling, Annett Heft and Mauricio Barahona: http://arxiv.org/abs/2405.07764

mcf

Code for the paper "Persistent Homology of the Multiscale Clustering Filtration" by Dominik J. Schindler and Mauricio Barahona: https://arxiv.org/abs/2305.04281

mfds-resources

multiscalecentrality

Graph centrality is a question of scale - Multiscale centrality (MSC) is a scale dependent measure of centrality on complex networks.

multiscalemobilitypatterns

Code for the paper "Multiscale mobility patterns and the restriction of human movement" by Dominik J Schindler, Jonathan M Clarke and Mauricio Barahona: https://arxiv.org/abs/2201.06323

optimalnavigation

Code from the paper "Collective Search With Finite Perception: Transient Dynamics and Search Efficiency" (2019, Front. Phys., https://doi.org/10.3389/fphy.2018.00153). Computes the time evolution of the ON model of interacting random walkers optimising their diffusion on a landscape over a finite-horizon using optimal transport.

pops

POPs: Propensity Optimised Paths

pygenstability

PyGenStability: Multiscale community detection with generalized Markov Stability

ramanspy

RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis

rmst

scihpf

single-cell Integrative Hierarchical Poisson Factorisation

severability

Code for the severability component quality function

stationary-bounds-for-continuous-time-chains

Code from the paper: "Bounding the stationary distributions of the chemical master equation via mathematical programming" J. Chem. Phys. 151, 034109 (2019); https://doi.org/10.1063/1.5100670

step

Contact tracing is a key tool in epidemiology to identify and control outbreaks of infectious diseases. Existing contact tracing methodologies produce linked networks of individuals based on a binary decision of contact which can be hampered by missing data and indirect contacts. Here, we present our Spatial-temporal Epidemiological Proximity (StEP) model to recover contact maps in disease outbreaks based on movement data. The StEP model accounts for imperfect data by considering probabilistic contacts between individuals based on spatial-temporal proximity of their movement trajectories, creating a robust movement network despite possible missing data and unseen transmission routes. We showcase the potential of StEP for contact tracing with outbreaks of multidrug-resistant bacterial infections and COVID-19 in a large hospital group in London, UK. In addition to the core structure of contacts that can be recovered using traditional methods of contact tracing, the StEP models are able to reveal missing contacts that connect seemingly separate outbreaks. Comparison with genomic data further confirmed that these additional contacts indeed improve characterisation of disease transmission and so highlights how the StEP framework can inform effective strategies of infection control and prevention.

streitberg-interaction

Repository for 'Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions'

thor-1

Code for https://arxiv.org/abs/2301.00790