Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities

By Pei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence. 2008

127 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mauricio A Álvarez
2. David B. Dunson
3. Neil D. Lawrence
4. Michael S. Steinbach
5. David Luengo
6. A. Herring
7. S. Pandalai
8. B. Baker

In Computational Theory and Mathematics

1. Mauricio A Álvarez
2. David Luengo
3. David B. Dunson
4. S. Pandalai
5. A. Herring
6. B. Baker
7. M. Wheeler
8. Neil D. Lawrence

In Molecular Biology

1. Mauricio A Álvarez
2. David B. Dunson
3. Neil D. Lawrence
4. Michael S. Steinbach
5. Vipin Kumar
6. Xiaowei Jia
7. Shaoming Xu
8. Jared D. Willard

Citations in the corpus

• Latent Force Models (2009, background): “Two further latent functions, shown in Figure 1 as latent forces 1 & 2, were consistent across all replicas: their shapes were time translated versions of the p53 profile as identified by (Barenco et al., 2006; Lawrence et al., 2007; Gao et al., 2008).”
• 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.”
• Mechanistic Hierarchical Gaussian Processes (2014, methodology): “One approach relies on the MAP-Laplace approximation (Gao et al. 2008), which finds the maximum a posteriori (MAP) estimate of the latent forcing function and then uses a Taylor approximation at that point.”
• Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “Alvarez et al. [10] draw inspiration from similar applications in bioinformatics [101, 165], which showed an increase in predictive ability in computational biology, motion capture, and geostatistics datasets.”
• Sparse Convolved Gaussian Processes for Multi-output Regression (2008, methodology): “For systems involving high amounts of coupled differential equations [4], the approach presented here is a reasonable way of obtaining approximate solutions and incorporating prior domain knowledge to the model.”