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BMC Bioinformatics|May 24, 2011
A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regressionAlfredo A Kalaitzis, Neil D Lawrence
Royal Society Open Science|August 22, 2024
Accelerating AI for science: open data science for scienceNeil D Lawrence, Jessica Montgomery
Bioinformatics (Oxford, England)|September 13, 2006
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activitiesGuido Sanguinetti, Neil D Lawrence, Magnus Rattray
IEEE Transactions on Pattern Analysis and Machine Intelligence|September 10, 2015
Fast Nonparametric Clustering of Structured Time-SeriesJames Hensman, Magnus Rattray, Neil D Lawrence
Bioinformatics (Oxford, England)|April 25, 2006
A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcriptionGuido Sanguinetti, Magnus Rattray, Neil D Lawrence
BMC Bioinformatics|August 22, 2013
Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clustersJames Hensman, Neil D Lawrence, Magnus Rattray
Methods in Molecular Biology (Clifton, N.J.)|November 30, 2012
Mining regulatory network connections by ranking transcription factor target genes using time series expression dataAntti Honkela, Magnus Rattray, Neil D Lawrence
Plos Computational Biology|January 14, 2012
Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studiesNicoló Fusi, Oliver Stegle, Neil D Lawrence
IEEE Transactions on Pattern Analysis and Machine Intelligence|September 21, 2013
Linear latent force models using Gaussian processesMauricio A Álvarez, David Luengo, Neil D Lawrence
Bioinformatics (Oxford, England)|August 11, 2005
Accounting for probe-level noise in principal component analysis of microarray dataGuido Sanguinetti, Marta Milo, Magnus Rattray, et al.
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