https://github.com/barahona-research-group/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

https://github.com/barahona-research-group/bayesfactorsimilarity

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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

https://github.com/barahona-research-group/BayesFactorSimilarity/blob/master/

Similarity measure for sparse time course data with Gaussian processes
================
Zijing Liu
2021-05-22

Introduction
------------
This repository contains MATLAB functions for modelling time course data with
Gaussian processes (GP) and computing a pair-wise similarity measure in the form of
a Bayes factor. It uses the GPML toolbox (http://www.gaussianprocess.org/gpml/code/matlab/doc/).

* BF_onehyp.m - a function for computing the pair-wise similarity matrix, where the hyperparameters are optimised for the whole dataset.
* BF_twohyp.m - a function for computing the pair-wise similarity matrix, where the hyperparameters are optimised for each pair of time courses.
* BF_async.m - a function for computing the pair-wise similarity matrix, where the hyperparameters are optimised for the whole dataset and the time courses are asynchronous.
* script_synthetic_data.m - script to test on the synthetic data
* script_gene_data.m - script to cluster the gene expression data

* lib/ - the required Matlab packages including:
	* GPML toolbox for Gaussian process
	* Ncut and ZPclustering for spectral clustering
	* InfoTheory toolbox for NMI

* R/ - contains the R code to compute the BHI z-score.
* gene_data.mat - it is the Matlab data file containing the gene expression data.

References
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Liu, Zijing, and Mauricio Barahona. "Similarity measure for sparse time course data based on Gaussian processes." arXiv preprint https://arxiv.org/abs/2102.12342 (2021). Accepted at 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021), July 27-29, 2021.  Link: https://proceedings.mlr.press/v161/liu21a.html

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