(Link out to University of St Andrews Research Profile)


The role of multidimensional poverty in antibiotic misuse: a mixed-methods study of self-medication and non-adherence in Kenya, Tanzania, and Uganda
Green DL, Keenan K, Fredricks KJ, Huque SI, Mushi MF, Kansiime C, Asiimwe B, Kiiru J, Mshana SE, Neema S, Mwanga JR, Kesby M, Lynch AG, Worthington H, Olamijuwon E, Abed Al Ahad M, Aduda A, Njeru JM, Mmbaga BT, Bazira J, Sandeman A, Stelling J, Gillespie SH, Kibiki G, Sabiiti W, Sloan DJ, Holden MTG & The HATUA Consortium The Lancet Global Health 11:e59–68 doi:10.1016/S2214-109X(22)00423-5


Treatment of missing data in Bayesian network structure learning: an application to linked biomedical and social survey data
Ke X, Keenan K & Smith, VA (2022) BMC Medical Research Methodology 22:326 doi:10.1186/s12874-022-01781-9

Practical application of a Bayesian network approach to poultry epigenetics and stress
Videla Rodriguez EA, Pértille F, Guerrero-Bosagna C, Mitchell JBO, Jensen P & Smith, VA (2022) BMC Bioinformatics 23:261 doi:10.1186/s12859-022-04800-0

N-strain epidemic model using bond percolation
Mann P, Smith VA, Mitchell JBO & Dobson S (2022) Physical Review E 106:014304 doi:10.1103/PhysRevE.106.014304.

A Bayesian network structure learning approach to identify genes associated with stress in spleens of chickens
Videla Rodriguez EA, Mitchell JBO & Smith VA (2022) Scientific Reports 12:7482 doi:10.1038/s41598-022-11633-7

Degree correlations in graphs with clique clustering
Mann P, Smith VA, Mitchell JBO & Dobson S (2022) Physical Review E 105:044314 doi:10.1103/PhysRevE.105.044314.

Bayesian Networks as a novel tool to enhance interpretability and predictive power of ecological models
E Hui, R Stafford, IM Matthews & VA Smith (2022) Ecological Informatics 68:101539 doi:10.1016/j.ecoinf.2021.101539


Genes identified via Bayesian network analysis of temporal dynamic expression are prognostic of survival in ovarian cancer
Currant H, Vogogias A & Smith VA (2021) Proceedings of the 15th Bayesian Modelling Applications Workshop

Symbiotic and antagonistic disease dynamics on clustered networks using bond percolation
Mann P, Smith VA, Mitchell JBO & Dobson S (2021) Physical Review E 104:024303 doi:10.1103/PhysRevE.104.024303

Exact formula for bond percolation on cliques
Mann P, Smith VA, Mitchell JBO, Jefferson CA & Dobson S (2021) Physical Review E 104:024304 Physical Review E 104:024303 doi:10.1103/PhysRevE.104.024304

Two-pathogen model with competition on clustered networks
Mann P, Smith VA, Mitchell JBO & Dobson S (2021) Physical Review E 103:062308 doi:10.1103/PhysRevE.103.062308

Cooperative coinfection dynamics on clustered networks
Mann P, Smith VA, Mitchell JBO & Dobson S (2021) Physical Review E 103:042307 doi:10.1103/PhysRevE.103.042307

Bayesian Network Analysis reveals resilience of the jellyfish Aurelia aurita to an Irish Sea regime shift
Mitchell EG, Wallace MI, Smith VA, Wiesenthal AA & Brierley AS (2021) Scientific Reports 11:3707. doi:10.1038/s41598-021-82825-w

Protocol for an interdisciplinary cross-sectional study investigating the social, biological and community-level drivers of antimicrobial resistance (AMR): Holistic Approach to Unravelling Antibiotic Resistance in East Africa (HATUA)
Asiimwe B, Kiiru J, Mshana S, Neema S, Kesby M, Mwanga JR, Sloan DJ, Mmbaga B, Smith VA, Gillespie SH, Lynch A, Sandeman AF, Stelling J, Elliott A, Aanensen D, Kibiki GE, Sabiiti W & Holden M (2021) BMJ Open 11:e04141. doi:10.1136/bmjopen-2020-041418

Percolation in random graphs with higher-order clustering
Mann P, Smith VA, Mitchell JBO & Dobson S (2021) Physical Review E 103:012313. doi:10.1103/PhysRevE.103.012313

Random graphs with arbitrary clustering and their applications
Mann P, Smith VA, Mitchell JBO & Dobson S (2021) Physical Review E 103:012309. doi:10.1103/PhysRevE.103.012309


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. doi:10.1145/3230623


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). doi:10.2312/cgvc.20161288

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. doi:10.1186/s12885-016-2212-6

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. doi:10.1002/9781119078845.ch27


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. doi:10.1038/srep15563

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. doi:10.1038/srep10775


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. doi:10.1038/bjc.2014.258

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. doi:10.1515/sagmb-2013-0051


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. doi:10.1093/czoolo/59.3.403


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. doi:10.1016/j.ecoinf.2012.05.002


Biology students building computer simulations using StarLogo TNG
Smith VA, Duncan I (2011) Bioscience Education 18: 6. doi:10.3108/beej.18.6SE

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. doi:10.3233/ACP-2011-0003


Revealing ecological networks using Bayesian network inference algorithms
Milns I, Beale CM, Smith VA (2010) Ecology 91: 1892-1899. doi:10.1890/09-0731.1

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. doi:10.1007/978-1-84996-396-1_9

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. doi:10.1002/9783527630271.CH13

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. doi:10.1007/978-1-60761-800-3_12

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. doi:10.1007/s10827-009-0174-2
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. doi:10.1055/s-0029-1216348


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. doi:10.1163/156853907782418259

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. doi:10.1371/journal.pcbi.0020161
[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. doi:10.1093/bioinformatics/bth448


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. doi:10.1007/s00359-002-0358-y

Evaluating functional network inference using simulations of complex biological systems
Smith VA, Jarvis ED, Hartemink AJ (2002) Bioinformatics 18: S216-S224. doi:10.1093/bioinformatics/18.suppl_1.S216

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. doi:10.1006/anbe.2001.1886


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. doi:10.1006/anbe.2000.1531

Other Publications


A Code For Carolyn: A Genomic Thriller
Smith VA (2019) Science and Fiction, Springer. doi:10.1007/978-3-030-04553-1


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)