Dr Vinny Davies

  • Lecturer (Statistics)

Biography

Dr Davies is a lecturer in Statistics in the School of Mathematics and Statistics specilising in computational biology and computational methods for statistics and machine learning. He completed his Ph.D. within the School of Mathematics and Statistics where he focused on variable selection models for selecting antigenic sites in virus evolution. He then completed several post-docteral research positions in both the schools of Statistics and Computing Science, as well as spending time as a Biostatistician at the University of Leeds. He recently returned to the School of Mathematics and Statistics where his research interests will focus on methods on the interface between Statistics and Machine Learning. He has a particular interest in Computational Metabolomics, but a general interest in applying statistical and machine learning methods to any biological, chemical or health problem.

If you are interested in a doing a Ph.D., please take a look at the Additional Information section or email me directly.

Research interests

Research units

  • Statistics & Data Analytics
  • Bayesian modelling and inference
  • Computational statistics
  • Machine learning and AI
  • Environmental, ecological sciences and sustainability
  • Statistical modelling for biology, genetics and *omics

Publications

List by: Type | Date

Jump to: 2023 | 2022 | 2021 | 2020 | 2019 | 2017 | 2016 | 2014 | 2013
Number of items: 21.

2023

Wandy, J. , Mcbride, R., Rogers, S. , Terzis, N., Weidt, S., van der Hooft, J. J.J. , Bryson, K. , Daly, R. and Davies, V. (2023) Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics. Frontiers in Molecular Biosciences, 10, 1130781. (doi: 10.3389/fmolb.2023.1130781)

2022

Robinson, J. I. et al. (2022) Comprehensive genetic and functional analyses of Fc gamma receptors influence on response to rituximab therapy for autoimmunity. EBioMedicine, 86, 104343. (doi: 10.1016/j.ebiom.2022.104343) (PMID:36371989) (PMCID:PMC9663864)

Wandy, J. , Davies, V. , McBride, R., Weidt, S., Rogers, S. and Daly, R. (2022) ViMMS 2.0: A framework to develop, test and optimise fragmentation strategies in LC-MS metabolomics. Journal of Open Source Software, 7(71), 3990. (doi: 10.21105/joss.03990)

2021

Scott, E. M. , Davies, V. , Nolan, A. M., Noble, C. E., Dowgray, N. J., German, A. J., Wiseman-Orr, M. L. and Reid, J. (2021) Validity and responsiveness of the generic health-related quality of life instrument (VetMetrica™) in cats with osteoarthritis. Comparison of vet and owner impressions of quality of life impact. Frontiers in Veterinary Science, 8, 733812. (doi: 10.3389/fvets.2021.733812)

Davies, V. , Wandy, J. , Weidt, S., van der Hooft, J. J.J. , Miller, A. , Daly, R. and Rogers, S. (2021) Rapid development of improved data-dependent acquisition strategies. Analytical Chemistry, 93(14), pp. 5676-5683. (doi: 10.1021/acs.analchem.0c03895) (PMID:33784814) (PMCID:PMC8047769)

Davies, V. , Reid, J. and Scott, E. M. (2021) Optimisation of scores generated by an online feline health–related quality of life (HRQL) instrument to assist the veterinary user interpret its results. Frontiers in Veterinary Science, 7, 601304. (doi: 10.3389/fvets.2020.601304) (PMID:33490133) (PMCID:PMC7815521)

2020

Arnold, K. F., Davies, V. , de Kamps, M., Tennant, P. W.G., Mbotwa, J. and Gilthorpe, M. S. (2020) Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning. International Journal of Epidemiology, 49(6), pp. 2074-2082. (doi: 10.1093/ije/dyaa049) (PMID:32380551) (PMCID:PMC7825942)

Davies, V. , Scott, E. M. , Wright, A. K. and Reid, J. (2020) Development of an early warning system for owners using a validated health-related quality of life (HRQL) instrument for companion animals and its use in a large cohort of dogs. Frontiers in Veterinary Science, 7, 575795. (doi: 10.3389/fvets.2020.575795) (PMID:33195573) (PMCID:PMC7541963)

2019

Davies, V. , Noè, U. , Lazarus, A., Gao, H. , Macdonald, B. , Berry, C. , Luo, X. and Husmeier, D. (2019) Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(5), pp. 1555-1576. (doi: 10.1111/rssc.12374) (PMID:31762497) (PMCID:PMC6856984)

Wandy, J. , Davies, V. , van der Hooft, J. J.J. , Weidt, S., Daly, R. and Rogers, S. (2019) In silico optimization of mass spectrometry fragmentation strategies in metabolomics. Metabolites, 9(10), 219. (doi: 10.3390/metabo9100219) (PMID:31600991)

Davies, V. , Reid, J., Wiseman-Orr, M. L. and Scott, E. M. (2019) Optimising outputs from a validated online instrument to measure health-related quality of life (HRQL) in dogs. PLoS ONE, 14(9), e0221869. (doi: 10.1371/journal.pone.0221869) (PMID:31532799) (PMCID:PMC6750605)

Husmeier, D. , Lazarus, A., Noè, U. , Davies, V. , Borowska, A. , Macdonald, B. , Gao, H. , Berry, C. and Luo, X. (2019) Statistical Emulation of Cardiac Mechanics: an Important Step Towards a Clinical Decision Support System. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 29. ISBN 9781927877647 (doi: 10.11159/icsta19.29)

Davies, V. , Harvey, W. T., Reeve, R. and Husmeier, D. (2019) Improving the identification of antigenic sites in the H1N1 Influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(4), pp. 859-885. (doi: 10.1111/rssc.12338) (PMID:31598013) (PMCID:PMC6774336)

Noè, U. , Lazarus, A., Gao, H. , Davies, V. , Macdonald, B. , Mangion, K., Berry, C. , Luo, X. and Husmeier, D. (2019) Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: a critical step towards clinical end-user relevance. Journal of the Royal Society: Interface, 16(156), 20190114. (doi: 10.1098/rsif.2019.0114) (PMID:31266415) (PMCID:PMC6685034)

Moesta, A., Hours, M.A., Boutigny, L., Davies, V. , Kelly, S., Mougeotc, I. and Reid, J. (2019) A Weight Loss Diet With Alpha-casozepine and L-tryptophane Improves Health Related Quality of Life in Dogs. In: 12th International Veterinary Behaviour Meeting 2019, Washington, D.C., USA, 30 Jul - 01 Aug 2019, pp. 66-74. ISBN 9781527242326

2017

Davies, V. , Reeve, R. , Harvey, W. T., Maree, F. F. and Husmeier, D. (2017) A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution. Computational Statistics, 32(3), pp. 803-843. (doi: 10.1007/s00180-017-0730-6)

2016

Davies, V. , Reeve, R. , Harvey, W. T. and Husmeier, D. (2016) Selecting random effect components in a sparse hierarchical Bayesian model for identifying antigenic variability. In: Computational Intelligence Methods for Bioinformatics and Biostatistics: 12th International Meeting, CIBB 2015, Naples, Italy, September 10-12, 2015, Revised Selected Papers. Series: Lecture Notes in Computer Science (9874). Springer, pp. 14-27. ISBN 9783319443317 (doi: 10.1007/978-3-319-44332-4_2)

2014

Davies, V. , Reeve, R. , Harvey, W., Maree, F. and Husmeier, D. (2014) Sparse Bayesian variable selection for the identification of antigenic variability in the Foot-and-Mouth disease virus. Proceedings of Machine Learning Research, 33, pp. 149-158.

Davies, V. and Husmeier, D. (2014) Modelling transcriptional regulation with Gaussian processes. In: Valente, A. X.C.N., Sarkar, A. and Gao, Y. (eds.) Recent Advances in Systems Biology Research. Nova Science Publishers: New York, pp. 157-184. ISBN 9781629487366

2013

Chanialidis, C. , Craigmile, P., Davies, V. , Dean, N. , Evers, L. , Filiippone, M., Gupta, M. , Ray, S. and Rogers, S. (2013) Discussion of Henning and Liao: How to find an appropriate clustering for mixed type variables with application to socio-economic stratification. Journal of the Royal Statistical Society: Series C. 62, 309-369. Discussion Paper. Springer. (doi: 10.1111/j.1467-9876.2012.01066.x).

Davies, V. and Husmeier, D. (2013) Assessing the impact of non-additive noise on modelling transcriptional regulation with Gaussian processes. In: Muggeo, V.M.R., Capursi, V., Boscaino, G. and Lovison, G. (eds.) Proceedings of the 28th International Workshop on Statistical Modelling. Gruppo Istituto Poligrafico Europeo SRL, pp. 559-562. ISBN 9788896251492

This list was generated on Sun Mar 26 11:30:29 2023 BST.
Number of items: 21.

Articles

Wandy, J. , Mcbride, R., Rogers, S. , Terzis, N., Weidt, S., van der Hooft, J. J.J. , Bryson, K. , Daly, R. and Davies, V. (2023) Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics. Frontiers in Molecular Biosciences, 10, 1130781. (doi: 10.3389/fmolb.2023.1130781)

Robinson, J. I. et al. (2022) Comprehensive genetic and functional analyses of Fc gamma receptors influence on response to rituximab therapy for autoimmunity. EBioMedicine, 86, 104343. (doi: 10.1016/j.ebiom.2022.104343) (PMID:36371989) (PMCID:PMC9663864)

Wandy, J. , Davies, V. , McBride, R., Weidt, S., Rogers, S. and Daly, R. (2022) ViMMS 2.0: A framework to develop, test and optimise fragmentation strategies in LC-MS metabolomics. Journal of Open Source Software, 7(71), 3990. (doi: 10.21105/joss.03990)

Scott, E. M. , Davies, V. , Nolan, A. M., Noble, C. E., Dowgray, N. J., German, A. J., Wiseman-Orr, M. L. and Reid, J. (2021) Validity and responsiveness of the generic health-related quality of life instrument (VetMetrica™) in cats with osteoarthritis. Comparison of vet and owner impressions of quality of life impact. Frontiers in Veterinary Science, 8, 733812. (doi: 10.3389/fvets.2021.733812)

Davies, V. , Wandy, J. , Weidt, S., van der Hooft, J. J.J. , Miller, A. , Daly, R. and Rogers, S. (2021) Rapid development of improved data-dependent acquisition strategies. Analytical Chemistry, 93(14), pp. 5676-5683. (doi: 10.1021/acs.analchem.0c03895) (PMID:33784814) (PMCID:PMC8047769)

Davies, V. , Reid, J. and Scott, E. M. (2021) Optimisation of scores generated by an online feline health–related quality of life (HRQL) instrument to assist the veterinary user interpret its results. Frontiers in Veterinary Science, 7, 601304. (doi: 10.3389/fvets.2020.601304) (PMID:33490133) (PMCID:PMC7815521)

Arnold, K. F., Davies, V. , de Kamps, M., Tennant, P. W.G., Mbotwa, J. and Gilthorpe, M. S. (2020) Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning. International Journal of Epidemiology, 49(6), pp. 2074-2082. (doi: 10.1093/ije/dyaa049) (PMID:32380551) (PMCID:PMC7825942)

Davies, V. , Scott, E. M. , Wright, A. K. and Reid, J. (2020) Development of an early warning system for owners using a validated health-related quality of life (HRQL) instrument for companion animals and its use in a large cohort of dogs. Frontiers in Veterinary Science, 7, 575795. (doi: 10.3389/fvets.2020.575795) (PMID:33195573) (PMCID:PMC7541963)

Davies, V. , Noè, U. , Lazarus, A., Gao, H. , Macdonald, B. , Berry, C. , Luo, X. and Husmeier, D. (2019) Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(5), pp. 1555-1576. (doi: 10.1111/rssc.12374) (PMID:31762497) (PMCID:PMC6856984)

Wandy, J. , Davies, V. , van der Hooft, J. J.J. , Weidt, S., Daly, R. and Rogers, S. (2019) In silico optimization of mass spectrometry fragmentation strategies in metabolomics. Metabolites, 9(10), 219. (doi: 10.3390/metabo9100219) (PMID:31600991)

Davies, V. , Reid, J., Wiseman-Orr, M. L. and Scott, E. M. (2019) Optimising outputs from a validated online instrument to measure health-related quality of life (HRQL) in dogs. PLoS ONE, 14(9), e0221869. (doi: 10.1371/journal.pone.0221869) (PMID:31532799) (PMCID:PMC6750605)

Davies, V. , Harvey, W. T., Reeve, R. and Husmeier, D. (2019) Improving the identification of antigenic sites in the H1N1 Influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(4), pp. 859-885. (doi: 10.1111/rssc.12338) (PMID:31598013) (PMCID:PMC6774336)

Noè, U. , Lazarus, A., Gao, H. , Davies, V. , Macdonald, B. , Mangion, K., Berry, C. , Luo, X. and Husmeier, D. (2019) Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: a critical step towards clinical end-user relevance. Journal of the Royal Society: Interface, 16(156), 20190114. (doi: 10.1098/rsif.2019.0114) (PMID:31266415) (PMCID:PMC6685034)

Davies, V. , Reeve, R. , Harvey, W. T., Maree, F. F. and Husmeier, D. (2017) A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution. Computational Statistics, 32(3), pp. 803-843. (doi: 10.1007/s00180-017-0730-6)

Davies, V. , Reeve, R. , Harvey, W., Maree, F. and Husmeier, D. (2014) Sparse Bayesian variable selection for the identification of antigenic variability in the Foot-and-Mouth disease virus. Proceedings of Machine Learning Research, 33, pp. 149-158.

Book Sections

Davies, V. , Reeve, R. , Harvey, W. T. and Husmeier, D. (2016) Selecting random effect components in a sparse hierarchical Bayesian model for identifying antigenic variability. In: Computational Intelligence Methods for Bioinformatics and Biostatistics: 12th International Meeting, CIBB 2015, Naples, Italy, September 10-12, 2015, Revised Selected Papers. Series: Lecture Notes in Computer Science (9874). Springer, pp. 14-27. ISBN 9783319443317 (doi: 10.1007/978-3-319-44332-4_2)

Davies, V. and Husmeier, D. (2014) Modelling transcriptional regulation with Gaussian processes. In: Valente, A. X.C.N., Sarkar, A. and Gao, Y. (eds.) Recent Advances in Systems Biology Research. Nova Science Publishers: New York, pp. 157-184. ISBN 9781629487366

Davies, V. and Husmeier, D. (2013) Assessing the impact of non-additive noise on modelling transcriptional regulation with Gaussian processes. In: Muggeo, V.M.R., Capursi, V., Boscaino, G. and Lovison, G. (eds.) Proceedings of the 28th International Workshop on Statistical Modelling. Gruppo Istituto Poligrafico Europeo SRL, pp. 559-562. ISBN 9788896251492

Research Reports or Papers

Chanialidis, C. , Craigmile, P., Davies, V. , Dean, N. , Evers, L. , Filiippone, M., Gupta, M. , Ray, S. and Rogers, S. (2013) Discussion of Henning and Liao: How to find an appropriate clustering for mixed type variables with application to socio-economic stratification. Journal of the Royal Statistical Society: Series C. 62, 309-369. Discussion Paper. Springer. (doi: 10.1111/j.1467-9876.2012.01066.x).

Conference Proceedings

Husmeier, D. , Lazarus, A., Noè, U. , Davies, V. , Borowska, A. , Macdonald, B. , Gao, H. , Berry, C. and Luo, X. (2019) Statistical Emulation of Cardiac Mechanics: an Important Step Towards a Clinical Decision Support System. In: International Conference on Statistics: Theory and Applications (ICSTA’19), Lisbon, Portugal, 13-14 Aug 2019, p. 29. ISBN 9781927877647 (doi: 10.11159/icsta19.29)

Moesta, A., Hours, M.A., Boutigny, L., Davies, V. , Kelly, S., Mougeotc, I. and Reid, J. (2019) A Weight Loss Diet With Alpha-casozepine and L-tryptophane Improves Health Related Quality of Life in Dogs. In: 12th International Veterinary Behaviour Meeting 2019, Washington, D.C., USA, 30 Jul - 01 Aug 2019, pp. 66-74. ISBN 9781527242326

This list was generated on Sun Mar 26 11:30:29 2023 BST.

Supervision

Current MRes Students

  • Cara MacBride

Current PhD Students

  • Ross McBride
  • Davison, Emily
    Designing advanced statistical inference methods for learning the parameters of a mathematical biodiversity model

Teaching

I generally teach on the introductory Python courses as well as the Large Scale Computing course (NNs in Tensorflow), as well as supervise a number of undergraduate and master's projects.

Research datasets

Jump to: 2022 | 2021 | 2019
Number of items: 3.

2022

Wandy, J. , McBride, R., Weidt, S., Rogers, S. , Terzis, N., van der Hooft, J. J. J., Bryson, K. , Daly, R. and Davies, V. (2022) DDA and DIA data for ViMMS 2.0. [Data Collection]

2021

Davies, V. , Wandy, J., Weidt, S., van der Hooft, J., Miller, A. , Daly, R. and Rogers, S. (2021) Rapid Development of Improved Data-Dependent Acquisition Strategies. [Data Collection]

2019

Wandy, J., Davies, V. , van der Hooft, J., Weidt, S., Daly, R. and Rogers, S. (2019) In-silico Optimisation of Mass Spectrometry Fragmentation Strategies in Metabolomics. [Data Collection]

This list was generated on Sat Mar 25 16:57:27 2023 GMT.

Additional information

I am looking for potential PhD students across a range of subjects and have a number of projects available below. Please contact me if you wish to discuss these or any other projects further.

Metabolomics DIA Resolver

In metabolomics we take a sample (blood, urine, etc) and put it through a mass spectrometer. The mass spectrometer scans the sample in multiple ways to help us work out what metabolites can be found in the sample. Identifying these metabolites can be useful for clinical trials, disease diagnosis and progression and various other medical applications. There are various way of choosing the scans, but in one particular method (DIA) we often see multiple fragments from multiple metabolites in a single scan. In order to identify the metabolites we need to work out which fragments belong to which metabolites. The project will use our recently developed virtual mass spectrometer, ViMMS (Wandy et al., 2019; Wandy et al., 2022), to continue the development of our new metabolomics DIA resolver, MSdeconvolve. We will expand MSdeconvole to work across multiple repeated samples collected in different ways and then extended it to work for completely different samples. Initially this will be done using standard statistical and machine learning methods, but we will look to extend this into a Bayesian modelling framework.

Scalable Bayesian Models for Inferring Evolutionary Traits of Plants

Supervised jointly with Richard Reeve and Claire Harris

The functional traits and environmental preferences of plant species determine how they will react to changes resulting from global warming. The main global biodiversity repositories, such as the Global Biodiversity Information Facility (GBIF), contain hundreds of millions of records from hundreds of thousands of species in the plant kingdom alone, and the spatiotemporal data in these records can be associated with soil, climate or other environmental data from other databases. Combining these records allow us to identify environmental preferences, especially for common species where many records exist. Furthermore, in a previous PhD studentship we showed that these traits are highly evolutionarily conserved (Harris et al., 2022), so it is possible to impute the preferences for rare species where little data exists using phylogenetic inference techniques.

The aim of this PhD project is to investigate the application of Bayesian variable selection methods to identify these evolutionarily conserved traits more effectively, and to quantify these traits and their associated uncertainty for all plant species for use in a plant ecosystem digital twin that we are developing separately to forecast the impact of climate change on biodiversity. In another PhD studentship, we previously developed similar methods for trait inference in viral evolution (Davies et al., 2017; Davies et al., 2019), but due to the scale of the data here, these methods will need to be significantly enhanced. We therefore propose a project to investigate extensions to methods for phylogenetic trait inference to handle datasets involving hundreds of millions of records in phylogenies with hundreds of thousands of tips, potentially through either sub-sampling (Quiroz et al., 2018) or modelling splitting and recombination (Nemeth & Sherlock, 2018).

Gaussian Process Emulation for Mathematical Models of the Heart

Supervised jointly with Benn Macdonald, Mu Nui, and Hao Gao

Mathematical models of the heart can help us understand how the heart functions and provide us with valuable insights into how we can treat patients or diagnose disease. Previous and ongoing work has looked at how we can use statistical and machine learning emulation strategies to speed up inference and make the mathematical models applicable within a clinical setting. The aim of this project is to further develop these methods through the application of Gaussian Processes and apply them to different mathematical problems with higher dimensional inputs. In many of the possible applications, the mathematical models will often have high dimensional and potentially correlated parameter inputs, as well as highly correlated outputs. The initial aim of this work will be to further develop the emulation methods to deal with these problems and look at how we can more effectively select the parameter inputs for the simulations we choose to generate output for. Further work will then look at how these models can potentially be combined with other techniques such as automated annotation, accelerating the construction of our emulator, or through the combination of other emulators, which would allow for the modelling of a more global system.