Modelling transcriptional regulation using Gaussian Processes

By Neil D. Lawrence, G. Sanguinetti, Magnus Rattray. 2006

127 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Control functionals for Monte Carlo integration (2014, methodology): “uld be to induce a covariance function k 0 that is consistent with solutions of the PDE in Eqn. 11. For simplicity consider the one-dimensional case (d= 1). Following recent work in this direction by Lawrence et al. (2007); Wheeler et al. (2014), we rewrite Eqn. 12 as ˚(x) = Z G(x;x0)[f(x0) ]dx0 (21”
Machine learning of linear differential equations using Gaussian processes (2017, methodology): “A similar approach has been adopted in [26], and further developed in the later work of [3].”
Latent Force Models (2009, methodology): “The notation used, f:,r, indicates the r-th column of F, and represents the values of that function for the r-th dimension at the times given by t.”
Efficient Sampling for Gaussian Process Inference using Control Variables (2008, methodology): “biology model where a set of genes is regulated by a transcription factor protein [8].”
Remarks on multi-output Gaussian process regression (2018, background): “Note that the base process can be extended as a general Gaussian process rather than a white noise [59, 60].”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “applications in bioinformatics [101, 165], which showed an increase in predictive ability in computational biology, motion capture, and geostatistics datasets.”
Computationally Efficient Convolved Multiple Output Gaussian Processes (2011, methodology): “…that the outputs are invariant under invertible linear transformations of the und rlying factor processes (this matrix is similar in spirit to the sensitivity matrix of Lawrence et al. (2007) and it is given a particular form so that not all latent processes affect the whole set of outputs).”
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels (2010, methodology): “A model with Rs = 1 and Ro = 0 was proposed by Lawrence et al. (2007) to describe protein transcription regulation in a single input motif (SIM) gene network.”
Mechanistic Hierarchical Gaussian Processes (2014, methodology): “(4) Here G(t, ξ ) is Green’s function that is used to explicitly calculate the covariance kernel for latent force models (Lawrence, Sanguinetti, and Rattray 2007; Álvarez, Luengo-Garcia, and Lawrence 2009; Honkela et al. 2010; Álvarez, Luengo, and Lawrence 2013).”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “Alvarez et al. [4] draws inspiration from similar applications in bioinformatics [88, 146], which showed an increase in predictive ability in computational biology, motion capture, and geostatistics datasets.”