Walter R. Gilks - researchr alias

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Viewing Publication 1 - 14 from 14

2023

Wavelet Monte Carlo: a principle for sampling from complex distributionsWalter R. Gilks, Lukas Cironis, Stuart Barber. sac, 33(5):92, October 2023. [doi]

2007

2006

Studying statistical properties of regulatory DNA sequences, and their use in predicting regulatory regions in the eukaryotic genomesIrina I. Abnizova, Walter R. Gilks. bib, 7(1):48-54, 2006. [doi]

Transcription Binding Site Prediction Using Markov ModelsIrina I. Abnizova, Alistair G. Rust, Mark Robinson, Rene te Boekhorst, Walter R. Gilks. jbcb, 4(2):425-442, 2006. [doi]

A novel algorithm and web-based tool for comparing two alternative phylogenetic treesTom M. W. Nye, Pietro Liò, Walter R. Gilks. bioinformatics, 22(1):117-119, 2006. [doi]

2005

Some statistical properties of regulatory DNA sequences, and their use in predicting regulatory regions in the ::::Drosophila ::::genome: the fluffy-tail testIrina I. Abnizova, Rene te Boekhorst, Klaudia Walter, Walter R. Gilks. bmcbi, 6:109, 2005. [doi]

Fusing microarray experiments with multivariate regressionWalter R. Gilks, Brian D. M. Tom, Alvis Brazma. eccb 2005: 143 [doi]

Probabilistic annotation of protein sequences based on functional classificationsEmmanuel D. Levy, Christos A. Ouzounis, Walter R. Gilks, Benjamin Audit. bmcbi, 6:302, 2005. [doi]

Statistical analysis of domains in interacting protein pairsTom M. W. Nye, Carlo Berzuini, Walter R. Gilks, M. Madan Babu, Sarah A. Teichmann. bioinformatics, 21(7):993-1001, 2005. [doi]

Quality determination and the repair of poor quality spots in array experimentsBrian D. M. Tom, Walter R. Gilks, Elizabeth T. Brooke-Powell, James W. Ajioka. bmcbi, 6:234, 2005. [doi]

2002

Modeling the percolation of annotation errors in a database of protein sequencesWalter R. Gilks, Benjamin Audit, Daniela De Angelis, Sophia Tsoka, Christos A. Ouzounis. bioinformatics, 18(12):1641-1649, 2002.

2001

RESAMPLE-MOVE Filtering with Cross-Model JumpsCarlo Berzuini, Walter R. Gilks. In Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science, pages 117-138, Springer, 2001. [doi]

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