23 citations · 25 across the 5 of their papers we have counts for
7 papers
A stochastic network approach to clustering and visualising single-cell genomic count data
Thomas E. Bartlett, Swati Chandna, Sandipan Roy
Important tasks in the study of genomic data include the identification of groups of similar cells (for example by clustering), and visualisation of data summaries (for example by…
Two-way sparsity for time-varying networks, with applications in genomics
Thomas E. Bartlett, Ioannis Kosmidis, Ricardo Silva
We propose a novel way of modelling time-varying networks, by inducing two-way sparsity on local models of node connectivity. This two-way sparsity separately promotes sparsity acr…
Co-modularity and Detection of Co-communities
Thomas E. Bartlett
This paper introduces the notion of co-modularity, to co-cluster observations of bipartite networks into co-communities. The task of co-clustering is to group together nodes of one…
A Power Variance Test for Nonstationarity in Complex-Valued Signals
Thomas E. Bartlett, Adam M. Sykulski, Sofia C. Olhede +2
We propose a novel algorithm for testing the hypothesis of nonstationarity in complex-valued signals. The implementation uses both the bootstrap and the Fast Fourier Transform such…
Detection of Epigenomic Network Community Oncomarkers
Thomas E. Bartlett, Alexey Zaikin
In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation ref…
Network inference and community detection, based on covariance matrices, correlations and test statistics from arbitrary distributions
Thomas E. Bartlett
In this paper we propose methodology for inference of binary-valued adjacency matrices from various measures of the strength of association between pairs of network nodes, or more…
Parenclitic network analysis of methylation data for cancer identification
Alexander Karsakov, Thomas Bartlett, Iosif Meyerov +2
We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development.…