ArticlePloS one2013
Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- BELMM: Bayesian model selection and random walk smoothing in time-series clustering.Bioinformatics (Oxford, England) · 2023Article
- Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics.Statistical applications in genetics and molecular biology · 2019Article
- A Survey of Data Mining and Deep Learning in Bioinformatics.Journal of medical systems · 2018Review
- A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables.PloS one · 2015Article
- Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.BMC bioinformatics · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.