(Link out to University of St Andrews Research Profile)


BayesPiles: visualisation support for Bayesian network structure learning
Vogogias A, Kennedy J, Archaumbault D, Bach B, Smith VA & Currant H (2018) ACM Transactions on Intelligent Systems and Technology 10:5


MLCut: exploring Multi-Level Cuts in dendrograms for biological data
Vogogias A, Kennedy J, Archaumbault D, Smith VA & Currant H (2016) Proceedings of Computer Graphics & Visual Computing (CGVC) 2016 (Turkay C, Wan TR, eds; Eurographics Association)

Dynamic modulation of phosphoprotein expression in ovarian cancer xenograft models
Koussounadis A, Langdon SP, Um IH, Kay C, Francis KE, Harrison DJ & Smith VA (2016) BMC Cancer 16:205

Biological network inference at multiple scales: from gene regulation to species interactions
Aderhold A, Smith VA & Husmeier D (2015) In: Pattern Recognition in Computational Molecular Biology: Techniques and Approaches (Elloumi M, Iliopoulos CS, Wang JTL, Zomaya AY, eds; Wiley-Blackwell) pp525-554


Novel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosis
Verleyen W, Langdon SP, Faratian D, Harrison DJ, Smith VA (2015) Scientific Reports 5:15563

Relationship between differentially expressed mRNA and mRNA-protein correlations in a xenograft model system
Koussounadis A, Langdon SP, Um IH, Harrison DJ & Smith VA (2015) Scientific Reports 5:10775


Chemotherapy-induced dynamic gene expression changes in vivo are prognostic in ovarian cancer
Koussounadis A, Langdon SP, Harrison DJ & Smith VA (2014) British Journal of Cancer 110: 2975-2984

Inference of circadian regulatory networks
Grzegorczyk M, Aderhold A, Smith VA & Husmeier D (2014) Proceedings of the 2nd International Work-Conference on Bioinformatics and Biomedical Engineering pp1001-1014


Assessment of Regression Methods for inference of regulatory networks involved in circadian regulation
Aderhold A, Husmeier D, Smith VA, Millar AJ, Grzegorczyk M (2013) Proceedings of the 10th International Workshop on Computational Systems Biology pp 29-33

Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes
Aderhold A, Husmeier D, Smith VA (2013) Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) 2013, Journal of Machine Learning Research: Workshop and Conference Proceedings 31: 75-81.

Predicting ecological regime shift under climate change: new modelling and molecular-based approaches
Stafford R, Smith VA, Husmier D, Grima T, Guinn B (2013) Current Zoology 59: 403-417


Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data
Aderhold A, Husmeier D, Lennon JJ, Beale CM, Smith VA (2012) Ecological Informatics 11: 55-64.


Biology students building computer simulations using StarLogo TNG
Smith VA, Duncan I (2011) Bioscience Education 18: 6.

An analytical approach differentiates between individual and collective cancer invasion
Katz E, Verleyen W, Blackmore CG, Edward M, Smith VA, Harrison DJ (2011) Analytical Cellular Pathology 34: 35-48.


Revealing ecological networks using Bayesian network inference algorithms
Milns I, Beale CM, Smith VA (2010) Ecology 91: 1892-1899.

Revealing structure of complex biological systems using Bayesian networks
Smith VA (2010) In: Network Science: Complexity in Nature and Technology (Estrada E, Fox M, Higham DJ , Oppo G-L, eds; Springer) pp 185-204.

Some useful mathematical tools to transform microarray data into interactive molecular networks
Matthäus F, Smith VA, Gebicke-Haerter PJ (2010) In: Systems Biology in Psychiatric Research: From High-Throughput Data to Mathematical Modeling (Tretter F, Gebicke-Haerter PJ, Winterer G, Mendoza E, eds; John Wiley and Sons) pp 277-300.

Cancer systems biology
Faratian D, Bown JL, Smith VA, Langdon SP, Harrison DJ (2010) In: Systems Biology in Drug Discovery and Development: Methods and Protocols (Yan Q, ed; Springer) pp 245-263.

Causal pattern recovery from neural spike train data using the Snap Shot Score
Echtermeyer C, Smulders TV, Smith VA (2010) Journal of Computational Neuroscience 29: 231-252
Also published as: Echtermeyer et al (2009) Journal of Computational Neuroscience Online First: 31 July 2009.


Interactive molecular networks obtained by computer-aided conversion of microarray data from brains of alcohol-drinking rats
Matthäus F, Smith VA, Fogtman A, Sommer WH, Leonardi-Essmann F, Lourdusamy A, Reimers MA, Spanagel R, Gebicke-Haerter PJ (2009) Pharmacopsychiatry 42: S118-S128.


Evolving an agent-based model to probe behavioral rules in flocks of cowbirds
Smith VA (2008) Proceedings of the Eleventh International Conference on Artificial Life MIT Press, Cambridge, MA, pp 561-568.
[Version with noted errata corrected]


Testing measures of animal social association by computer simulation
White DJ, Smith VA (2007) Behaviour 144: 1447-1468.

The CARMEN e-Science pilot project: Neuroinformatics work packages
Smith LS, Austin J, Baker S, Borisyuk R, Eglen S, Feng J, Gurney K, Jackson T, Kaiser M, Overton P, Panzeri S, Quian Quiroga R, Schultz SR, Sernagor E, Smith VA, Smulders TV, Stuart L, Whittington M, Ingram C (2007) In: Proceediings of the UK e-Science Programme All Hands Meeting 2007 (SJ Cox, ed), National e-Science Centre, pp 591-598.


Computational inference of neural information flow networks
Smith VA, Yu J, Smulders TV, Hartemink AJ, Jarvis ED (2006) PLoS Computational Biology 2: e161.
[Supporting Protocol, Figures, Tables (PDF)]
[Supporting Video (PowerPoint)]


Advances to Bayesian network inference for generating causal networks from observational biological data
Yu J, Smith VA, Wang PP, Hartemink AJ, Jarvis ED (2004) Bioinformatics 20: 3594-3603.


Influence of network topology and data collection on network inference
Smith VA, Jarvis ED, Hartemink AJ (2003) Pacific Symposium on Biocomputing 8: 164-175.


Using Bayesian network inference algorithms to recover molecular genetic regulatory networks
Yu J, Smith VA, Wang PP, Hartemink AJ, Jarvis ED (2002) International Conference on Systems Biology 2002 (ICSB02), December 2002.

A framework for integrating the songbird brain
Jarvis ED, Smith VA, Wada K, Rivas MV, McElroy M, Smulders TV, Carninci P, Hayashizaki Y, Dietrich F, Wu X, McConnell P, Yu J, Wang PP, Hartemink AJ, Lin S (2002) Journal of Comparative Physiology A 188: 961-980.

Evaluating functional network inference using simulations of complex biological systems
Smith VA, Jarvis ED, Hartemink AJ (2002) Bioinformatics 18: S216-S224.

The context of social learning: association patterns in a captive flock of brown-headed cowbirds
Smith VA, King AP, West MJ (2002) Animal Behaviour 63: 23-35.


A role of her own: female cowbirds, Molothrus ater, influence the development and outcome of song learning
Smith VA, King AP, West MJ (2000) Animal Behaviour 60: 599-609.

Other Publications


A Code For Carolyn: A Genomic Thriller
Smith VA (2019) Science and Fiction, Springer.


1st Year Practicals: Their Role in Developing Future Bioscientists
Adams D, Arkle S, Bevan R, Boachie-Ansah G, Bradshaw T, Cameron G, Campbell A-M, Chamberlain M, Gibson A, Gowers D, Hayes M, Heritage J, Hollingsworth M, Hooper H, Hudson K, Hughes I, Lindsey N, Meskin S, Park J, Podesta T, Rattray J, Scott G, Shearer MC, Smalley H, Smith VA, Smith D, Tierney A, Todd M, Verran J, Wakeford C, Wilbraham J, Wilson J (2008) HEA Centre for Bioscience Report.


The scientific method: teaching the how of science and not just the what
Shearer MC, Smith VA (2007) Centre for Bioscience Bulletin 21: 8.
(Note authorship correction in Centre for Bioscience Bulletin 22: 2)