Neil Lawrence's Publications

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2017

Technical Reports

2016

Journal Papers

Conference Papers

Technical Reports

2015

Journal Papers

    Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays

    Antti Honkela, Jaakko Peltonen, Hande Topa, Iryna Charapitsa, Filomena Matarese, Korbinian Grote, Hendrik G. Stunnenberg, George Reid, Neil D. Lawrence, Magnus Rattray in Proc. Natl. Acad. Sci. USA 112 (42), pp 13115-13120

    A reverse-engineering approach to dissect post-translational modulators of transcription factor’s activity from transcriptional data

    Gennaro Gambardella, Ivana Peluso, Sandro Montefusco, Mukesh Bansal, Diego L. Medina, Neil D. Lawrence, Diego Bernardo in BMC Bioinformatics 16 (279)

Conference Papers

2014

Journal Papers

Conference Papers

Technical Reports

Miscellaneous

2013

Journal Papers

Conference Papers

Book Chapters

2012

Journal Papers

    Identifying Targets of Multiple Co-regulated Transcription Factors from Expression Time-series by Bayesian Model Comparison

    Michalis K. Titsias, Antti Honkela, Neil D. Lawrence, Magnus Rattray in BMC Systems Biology 6 (53)

    Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition

    Christopher A. Penfold, Paul E. Brown, Neil D. Lawrence, Alastair S. H. Goldman in PLoS Computat Biol 8 (5), pp 0-0

    Improved linear mixed models for genome-wide association studies

    Jennifer Listgarten, Christoph Lippert, Carl M. Kadie, Robert I. Davidson, Eleazar Eskin, David Heckerman in Nature Methods 9, pp 525-526

    A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models

    Neil D. Lawrence in Journal of Machine Learning Research 13

    Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies

    Nicoló Fusi, Oliver Stegle, Neil D. Lawrence in PLoS Computat Biol 8, pp 0-0

    Genome-wide occupancy links Hoxa2 to Wnt-$\beta$-catenin signaling in mouse embryonic development

    Ian J. Donaldson, Shilu Amin, James Hensman, Eva Kutejova, Magnus Rattray, Neil D. Lawrence, Andrew Hayes, Christopher M. Ward, Nicoletta Bobola in Nucleaic Acids Research 40 (9), pp 3390-4001

    Kernels for Vector-Valued Functions: A Review

    Mauricio A. Álvarez, Lorenzo Rosasco, Neil D. Lawrence in Foundations and Trends in Machine Learning 4 (3), pp 195-266

Conference Papers

Technical Reports

2011

Journal Papers

    Computationally Efficient Convolved Multiple Output Gaussian Processes

    Mauricio A. Álvarez, Neil D. Lawrence in Journal of Machine Learning Research 12, pp 1425-1466

    Demodulation as Probabilistic Inference

    Richard E. Turner, Maneesh Sahani in IEEE Transactions on Audio, Speech, and Language Processing 19 (), pp 2398-2411

    Overlapping Mixtures of Gaussian Processes for the Data Association Problem

    Miguel Lázaro Gredilla, Steven Van Vaerenbergh, Neil D. Lawrence in Pattern Recognition 10 (4)

    A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression

    Alfredo A. Kalaitzis, Neil D. Lawrence in BMC Bioinformatics 12 (180)

    tigre: Transcription factor inference through Gaussian process reconstruction of expression for Bioconductor

    Antti Honkela, Pei Gao, Jonatan Ropponen, Magnus Rattray, Neil D. Lawrence in Bioinformatics 27, pp 1026-1027

Conference Papers

Book Chapters

Technical Reports

2010

Journal Papers

    Model-based Method for Transcription Factor Target Identification with Limited Data

    Antti Honkela, Charles Girardot, E. Hilary Gustafson, Ya-Hsin Liu, Eileen E. M. Furlong, Neil D. Lawrence, Magnus Rattray in Proc. Natl. Acad. Sci. USA 107 (17), pp 7793-7798

    Elementary properties of CaV1.3 Ca2+ channels expressed in mouse cochlear inner hair cells

    Valeria Zampini, Stuart Leigh Johnson, Christoph Franz, Neil D. Lawrence, Stefan Muenkner Jutta Engel, Marlies Knipper, Jacopo Magistretti, Sergio Masetto, Walter Marcotti in The Journal of Physiology 588, pp 187-189

    TFInfer: a tool for probabilistic inference of transcription factor activities

    H. M. Shahzad Asif, Matthew D. Rolfe, Jeff Green, Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti in Bioinformatics 26, pp 2635-2636

Conference Papers

Book Chapters

    Introduction to Learning and Inference in Computational Systems Biology

    Neil D. Lawrence in Chapter 1 of Learning and Inference in Computational Systems Biology (, , , eds)

    Gaussian Processes for Missing Species in Biochemical Systems

    Neil D. Lawrence, Magnus Rattray, Pei Gao, Michalis K. Titsias in Chapter 9 of Learning and Inference in Computational Systems Biology (, , , eds)

    A Brief Introduction to Bayesian Inference

    Neil D. Lawrence, Magnus Rattray in Chapter 5 of Learning and Inference in Computational Systems Biology (, , , eds)

Technical Reports

2009

Journal Papers

Conference Papers

    Efficient Sampling for Gaussian Process Inference using Control Variables

    Michalis K. Titsias, Neil D. Lawrence, Magnus Rattray in Advances in Neural Information Processing Systems (, , , eds), pp 1681-1688

    Non-Linear Matrix Factorization with Gaussian Processes

    Neil D. Lawrence, Raquel Urtasun in Proceedings of the International Conference in Machine Learning (, eds)

    Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery

    John Darby, Baihua Li, Nicholas Costen, David J. Fleet, Neil D. Lawrence in British Machine Vision Conference

    Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes

    Ben Calderhead, Mark Girolami, Neil D. Lawrence in Advances in Neural Information Processing Systems (, , , eds), pp 217-224

    Latent Force Models

    Mauricio A. Álvarez, David Luengo, Neil D. Lawrence in Proceedings of the Twelfth International Workshop on Artificial Intelligence and Statistics (, eds), pp 9-16

    Sparse Convolved Gaussian Processes for Multi-output Regression

    Mauricio A. Álvarez, Neil D. Lawrence in Advances in Neural Information Processing Systems (, , , eds), pp 57-64

Technical Reports

2008

Journal Papers

    Probabilistic approach to detecting dependencies between data sets

    Aarto Klami, Sami Kaski in Neurocomputing 72, pp 39-46

    Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities

    Pei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence in Bioinformatics 24, pp 0-0

Conference Papers

2007

Conference Papers

    Model-driven detection of Clean Speech Patches in Noise

    Jonathan Laidler, Martin Cooke, Neil D. Lawrence in Proceedings of Interspeech 2007

    Modelling transcriptional regulation using Gaussian Processes

    Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray in Advances in Neural Information Processing Systems (, , eds), pp 785-792

    Learning for Larger Datasets with the Gaussian Process Latent Variable Model

    Neil D. Lawrence in Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics (, eds), pp 243-250

    Hierarchical Gaussian Process Latent Variable Models

    Neil D. Lawrence, Andrew J. Moore in Proceedings of the International Conference in Machine Learning ( ed.), pp 481-488

    WiFi-SLAM Using Gaussian Process Latent Variable Models

    Brian D. Ferris, Dieter Fox, Neil D. Lawrence in Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007) ( ed.), pp 2480-2485

    Gaussian Process Latent Variable Models for Fault Detection

    Luka Eciolaza, M. Alkarouri, Neil D. Lawrence, Visakan Kadirkamanathan, Peter J. Fleming in Computational Intelligence and Data Mining, pp 287-292

Miscellaneous

2006

Journal Papers

Conference Papers

    Identifying submodules of cellular regulatory networks

    Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence in International Conference on Computational Methods in Systems Biology

    Missing Data in Kernel PCA

    Guido Sanguinetti, Neil D. Lawrence in ECML, Berlin, 2006, pp 751-758

    Local Distance Preservation in the GP-LVM through Back Constraints

    Neil D. Lawrence, Joaquin Quiñonero Candela in Proceedings of the International Conference in Machine Learning (, eds), pp 513-520

    Fast Variational Inference for Gaussian Process Models through KL-Correction

    Nathaniel J. King, Neil D. Lawrence in ECML, Berlin, 2006, pp 270-281

Book Chapters

Technical Reports

2005

Journal Papers

Conference Papers

Book Chapters

Technical Reports

Miscellaneous

2004

Journal Papers

Conference Papers

    Learning to Learn with the Informative Vector Machine

    Neil D. Lawrence, John C. Platt in Proceedings of the International Conference in Machine Learning (, eds), pp 512-519

    Gaussian Process Models for Visualisation of High Dimensional Data

    Neil D. Lawrence in Advances in Neural Information Processing Systems (, , eds), pp 329-336

    Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle

    Yasser H. Abdel-Haleem, Steve Renals, Neil D. Lawrence in International Conference on Acoustics, Speech and Signal Processing

Technical Reports

2003

Journal Papers

Conference Papers

    Variational Inference for Visual Tracking

    Jaco Vermaak, Neil D. Lawrence, Patrick Pérez in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 773-780

    A Variational Approach to Robust Bayesian Interpolation

    Michael E. Tipping, Neil D. Lawrence in Neural Networks for Signal Processing XIII (, , , , , eds), pp 229-238

    Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

    Matthias Seeger, Christopher K. I. Williams, Neil D. Lawrence in Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics (, eds)

    Bayesian Processing of Microarray Images

    Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier in Neural Networks for Signal Processing XIII (, , , , , eds), pp 71-80

    Fast Sparse Gaussian Process Methods: The Informative Vector Machine

    Neil D. Lawrence, Matthias Seeger, Ralf Herbrich in Advances in Neural Information Processing Systems (, , eds), pp 625-632

Technical Reports

2002

Conference Papers

Technical Reports

2001

Journal Papers

Conference Papers

Technical Reports

Miscellaneous

2000

Technical Reports

1999

Technical Reports

1998

Conference Papers

Technical Reports

0001

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