AnglePose: Robust, Precise Capacitiv Touch Tracking via 3D Orientation Estimation. Simon Rogers, John Williamson, Criag Stewart and Roderick Murray-Smith.Proceedings of the 98th international conference on Human Factors in Computing Systems, CHI2011.
A First Course in Machine Learning. Simon Rogers and Mark Girolami. Chapman and Hall / CRC press. More details here.
Statistical methods and models for bridging Omics data levels". Simon Rogers. Chapter in Bioinformatics for Omics data: Methods and Protocols, Springer Humana, edited by Dr. Bernd Mayer.buy it here!
Bayesian Approaches for Mass Spectrometry-Based Metabolomics. Simon Rogers, Richard A. Scheltema, Michael Barrett and Rainer Breitling. Chapter in Handbook of Statistical Systems Biology. Edited by Michael P. H. Stumpf, David. J. Balding and Mark Girolami. Wiley. Publishers site.

FingerCloud: Uncertainty and autonomy handover in capactive sensing CHI 2010, Proceedings of the 28th international conference on Human Factors in Computing Systems doi:10.1145/1753326.1753412, available here
Protein Interaction Detection in Sentences via Gaussian Processes: A preliminary evaluation T. Polajnar, S. Rogers and M. Girolami International Journal of Data Mining and Bioinformatics To appear.

Infinite Factorization of Multiple Non-parametric Views S. Rogers, A. Klami, J. Sinkkonen, M. Girolami and S. Kaski Machine Learning Online access
Classification of Protein Interaction Sentences via Gaussian Processes T. Polajnar, S. Rogers and M. Girolami Proceesings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics Lecture Notes in Bioinformatics 5780, pp282--292, 2009.[Available online]
Semi-parametric analysis of multi-rater data S. Rogers, M. Girolami, T. Polajnar Statistics and Computing To appear. Early Access.
Probabilistic assignment of formulas to mass peaks in metabolomics experiments S. Rogers, R.E. Scheltema, M. Girolami, R. Breitling Bioinformatics 25(4) 2009, 512--518[Available online] [journal website]

Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models S. Rogers, M. Girolami, W. Kolch, K.M. Waters, T.Liu, B. Thrall, H.S. Wiley Bioinformatics 24(24) 2008, 2894-2900[Available online] [journal website]

Multi-class semi-supervised learning with the e-truncated multinomial probit Gaussian process S. Rogers, M. Girolami Journal of Machine Learning Research: Workshop and Conference Proceedings 1, (Gaussian Processes in Practice) 17-32 [.pdf] [journal]
Bayesian model-based inference of transcription factor activity S. Rogers, R. Khanin, M. Girolami BMC Bioinformatics 8(2), special issue from PMSB workshop, Helsinki, June 2006 [.pdf] [journal]

Variational Multinomial Regression with Gaussian Process Priors M. Girolami, S. Rogers Neural Compuation 18(8):1790--1817 [.pdf] [journal]
Identification of Prognostic Signatures in Breast Cancer Microarray Data using Bayesian Techniques L. Carrivick, S. Rogers, J. Clark, C. Campbell, M. Girolami and C. Cooper Journal of The Royal Society Interface 3(8):351--469 [.pdf] [journal]

Hierarchic Bayesian Models for Kernel Learning M. Girolami, S. Rogers 22nd International Conference on Machine Learning (ICML 2005) 241--248 [.pdf] [Supplementary Material]
Disease diagnosis from Capillary Electophoresis: Mass Spectrometry S. Rogers, M. Girolami, R. Krebbs, H. Mischak Proceedings of the International Conference on Advances in Pattern Recognition, Bath 2005 183--191 [.pdf]
A Bayesian Regression Approach to the Inference of Regulatory Networks from Gene Expression Data S. Rogers, M. Girolami Bioinformatics 21(14):3131--3137 [.pdf] [Supplementary Info]
The Latent Process Decomposition of cDNA Microarray Data S. Rogers, M. Girolami, R. Breitling, C.Campbell IEEE/ACM Transactions on Computational Biology and Bioinformatics 2(2):143--156 [.pdf] [Supplementary Info]

Prognostic Classification of Relapsing Favorable Histology Wilms Tumor Using cDNA Microarray Expression Profiling and Support Vector Machines R. Williams, S. Hing, B. Greer, C. Whiteford, J. Wei, R. Natrajan, A. Kelsey, S. Rogers, C. Campbell, C. Pritchard-Jones, J. Khan Genes, Chromosomes and Cancer 41:65--79 [.pdf]

Expression profiling targeting chromosomes for tumor classification and prediction of clinical behavior. Y. Lu, D. Williamson, B. Wang, B. Summersgill, S. Rodriguez, S. Rgers, C. Pritchard-Jones, C. Campbell, J. Shipley Genes, Chromosomes and Cancer 38(3): 207--214 l; [.pdf]
Estimating Sample Size Requirements for Classifying Gene Expression Data. S. Mukherjee, P. Tamayo, S. Rogers, R. Rifkin, A. Engel, C. Campbell, T. Golub, J. Mesirov Journal of Computational Biology 10(2):119--142 [.pdf]

Book Chapters

Class Prediction with Microarray Datasets. S. Rogers, R. Williams, C.Campbell Bioinformatics using Computational Intelligence paradigms, Series: Studies in Fuzziness and Soft Computing, Vol. 176 Seiffert, Udo; Jain, Lakhmi C.; Schweizer, Patric (Eds.). Springer-Verlag 2005, 119--142

PhD Thesis

Machine learning techniques for microarray analysis. S. Rogers, Department of Engineering Mathematics, University of Bristol [.pdf.gz]