Publication List for Daniel Lawson.
Peer reviewed publications (by date):
20. Statistical frameworks for detecting tunnelling in cyber defence using big data. Daniel Lawson, Patrick Rubin-Delanchy, Niall Adams and Nicholas Heard. 2014. IEEE Joint Intelligence and Security Informatics Conference (JISIC/EISIC).
19. An approximate framework for flexible network flow screening. Niall Adams and Daniel Lawson. 2014. IEEE Joint Intelligence and Security Informatics Conference (JISIC/EISIC).
18. Filtering automated polling traffic in computer network flow data. Nicholas Heard, Daniel Lawson and Patrick Rubin-Delanchy. 2014. IEEE Joint Intelligence and Security Informatics Conference (JISIC/EISIC).
17. Three statistical approaches to sessionizing network flow data. Patrick Rubin-Delanchy, Daniel Lawson, Melissa Turcotte, Niall Adams and Nicholas Heard. 2014. IEEE Joint Intelligence and Security Informatics Conference (JISIC/EISIC).
16. Genome-wide analysis of cold adaption in indigenous Siberian populations, Alexia Cardona, Luca Pagani, Tiago Antao, Daniel J. Lawson, Christina A. Eichstaedt, Bryndis Yngvadottir, Ma Than Than Shwe, Joseph Wee, Chris Tyler-Smith , Irene Gallego Romero, Srilakshmi Raj, Mait Metspalu, Richard Villems, Rasmus Nielsen, Eske Willerslev, Boris A. Malyarchuk, Miroslava V. Derenko and Toomas Kivisild. 2014. PLoS One e98076.
15. Apparent strength conceals instability in a model for the collapse of historical states, Daniel Lawson and Neeraj Oak, 2014. PLoS One 9:e96523. (preprint)
14. Performance comparison of renewable incentive schemes using optimal control, Neeraj Oak, Daniel Lawson and Alan Champneys. 2014. Energy 64:44-57.
13. Past acidification and recovery of surface waters, soils and ecology in the United Kingdom: Prospects for the future under current deposition and land use protocols, R.C. Helliwell, J. Aherne, G. MacDougall, T.R. Nisbet, D. Lawson, B.J. Cosby and C.D. Evans, 2014. Ecological Indicators, 37:381-395. DOI.
12. Similarity matrices and clustering algorithms for population identifcation using genetic data, Daniel Lawson and Daniel Falush, 2012. Annual Review of Human Genomics, 13: 337-361. (Preprint, Supplementary Material).
11. Inference of population structure using dense haplotype data, Daniel Lawson, Garrett Hellenthal, Simon Myers, and Daniel Falush, 2012. PLoS Genetics, Vol. 8(1): e1002453. (Preprint, Supporting Information).
10. Bayesian analysis of non-linear differential equation models with application to a gut microbial ecosystem, Daniel Lawson, Grietje Holtrop and Harry Flint. The Biometrical Journal, Vol. 53, 543-556, 2011. (PrePrint), (Supplementary Material)
8. Inference of Homologous Recombination in Bacteria Using Whole Genome Sequences. Xavier Didelot, Daniel Lawson, Aaron Darling and Daniel Falush, 2010. Genetics, Vol. 186, 1435-1449, 2010. (Supplementary Material)
7. The role of weak selection and high mutation rates in nearly neutral evolution, Daniel Lawson and Henrik Jensen, 2009. Journal Theoretical Biology, 257(4):696-703.
6. SimMLST: simulation of multi-locus sequence typing data under a neutral model, Xavier Didelot, Daniel Lawson and Daniel Falush, 2009. Bioinformatics, 2009 25: 1442-1444.
5. Understanding Clustering in Type Space Using Field Theoretic Techniques, Daniel Lawson and Henrik Jensen, 2007. Bull. Math. Biol. 70:1065-1081.
4. Neutral Evolution in a Biological Population as Diffusion in Phenotype Space: Reproduction with Local Mutation but without Selection, Daniel Lawson and Henrik Jensen, 2007. Phys. Rev. Lett. 98:098102.
3. The Tangled Nature model of Evolutionary Ecology: an overview, Simon Laird, Daniel Lawson and Henrik Jensen, Mathematical modeling of biological systems. Volume 2. Editors: Andreas Deutsch et al, 2007.
2. Diversity as a product of interspecial interactions, Daniel Lawson, Henrik Jensen and Kunihiko Kaneko, Journal of Theoretical Biology (2006). Vol. 243:299-307.
1. The Species Area Relationship and evolution, Daniel Lawson Henrik Jensen, Journal of Theoretical Biology, 2006, 241:590-600.
Book chapters and Miscellaneous:
* "Populations in statistical genetics modelling and inference", Daniel Lawson (2013), Chapter 3 of "Population in the Human Sciences; Concepts, Methods, Evidence", Editor: Kreager, Capelli, Ulijaszek & Winney, OUP. (preprint).
Publications in press:
Contact the first author for pre-prints.
"A general decision framework for structuring computation using Data Directional Scaling to process massive similarity matrices", Daniel J. Lawson and Niall M. Adams, (preprint)
Note that these are designed as visual aids for a presentation rather than as a complete article, and so might not give comprehensive explanations. Additionally, it is possible that some may have been mildly corrupted when converted to a web suitable form.
You're going to need a bigger boat: How to stop interesting population genetics models from being swallowed up by really big datasets. Given at UCL Population Genetics meeting (February 2014).
Populations in statistical genetics: What are they, and how can we infer them from whole genome data?. Given in Cambridge (January 2014).
All the genomes in the world: Scalable Bayesian Computation using Emulation. Given at MCMSki in Chamonix (January 2014).
Towards running complex models on big data: Working with "all the genomes in the world" without changing the model (too much). Given at the 'Bayesian Computation' workshop in Reading (November 2013).
Identifying fine population structure using genomic scale data, a reasonably comprehensive overview of FineSTRUCTURE with a little clustering and summary statistics. Given at the Centre for Causal Analyses in Translational Epidemiology (CAiTE) in Bristol (May 2012).
The Dirichlet Process In Population Inference, describing how the Dirichlet Process prior is used in population finding models (covering the DP and the hierarchical DP). Given at the Advances in Markov Chain Monte Carlo workshop at ICMS in Edinburgh (April 2012).
ChromoPainter and FineSTRUCTURE Inference of population structure using dense haplotype data, a short overview of finestructure given at Popgroup in Nottingham (January 2012).
The Dirichlet Process and genetics, a brief overview of how we use the Dirichlet Process in FineSTRUCTURE, given at a group meetin in Bristol (January 2012).
Models for the next generation of genetics data, relating our Bacterial genomics and finestructure work, given in the Stats department at Bath (October 2011).
Approximating the Bacterial Ancestral Recombination Graph, a joint talk with Xavier Didelot at the "Graphical Models and Genetic Applications" workshop in Warwick (April 2009).
Data and Complex Models, Imperial College (December 2008).
Nearly-neutral dynamics: when is selection not selective?, Marseille 11th Evolutionary Biology Meeting, (September 2008).
Inference for Ecological Process models of Gut Bacteria, Berlin Meeting (July 2008)
Modelling with uncertainty in the Magic Model, joint with Rachel Helliwell, Bangor (March 2008).
The implications of neutral evolution for neutral ecology, Macaulay Institute (November 2007)
Neutral evolution in a type space, Dresden ECMTB (October 2007)
An Analytical method for neutral evolution in a type space, University of Sussex (September 2007).
Some implications of neutral evolution for ecology, Macaulay Institute (March 2007).
Some implications of neutral evolution for ecology, Aberdeen Population Ecology Research Unit (APERU), Aberdeen University (March 2007).
Statistical Physics and a model of Biological Evolution (October 2006).
Effects of stochasticity and finite population sizes in a neutral model of evolution, given at BioSS, Aberdeen (2006).
Diffusion in Evolution: Nonselfaveraging and its implications, given to the MathBio group at Imperial (2005).
The PaintMyChromosomes.com website, a portal for the fineSTRUCTURE genetics software.
The ClonalOrigin website, a portal for our bacterial recombination detection software.
the Private Life of Bacteria: Inferring genetic history using the weak Ancestral Recombination Graph, a poster presented at the Graphical Models and Genetic Applications workshop in Warwick.