Modern statistical techniques for assessing and predicting herbicide performance
This project will develop modern regression methods for graphical data.
With the proliferation of screening tools for chemical testing, it is now possible to create vast databases of chemicals easily. On the other hand, the development of rigorous statistical methodology that can be used to analyse these large databases is in its infancy, and further development to facilitate chemical discovery is imperative. In this project, you will develop statistical models and methodology for assessing chemical compounds from their descriptive characteristics and their performance on screening tests, and accordingly compute a quantitative score for each chemical. You will apply this methodology to tackle real problems provided by Syngenta using data from their screening experiments on herbicides.
Typically, thousands of potential herbicides will undergo a sequence of screening tests (assay tests) in the lab and each time ineffective compounds will be discarded and the remaining are assessed against a more complex set of criteria, with the final few undergoing rigorous field trials. Evidently, the data from the early trials will exhibit high uncertainty and subjectivity.
Motivation and starting points for different possible developments in this project have been identified together with Syngenta. In the starting phase of the project, you will develop a model to predict the herbicide’s performance on each test using information such as dosage, plant species tested, and the chemical’s structure which can be presented as a graph. Modern regression methods such as support vector regression, neural networks, and Gaussian process regression will be explored. The model should exploit the relationships between plant species and families of chemicals to improve predictive performance.
In most applications, a herbicide is assessed against a range of criteria. Therefore a method to combine multiple criteria according to their significance for scoring each herbicide is required. This will extend the model from the first stage to multivariate predictors with correlated components which are suitably transformed to provide an aggregate score for each herbicide and consequently identify promising compounds and reduce lab testing.
This project will be at the interface of statistics and computer science with significant mathematical, methodological, and computational components.
The successful student will be given privileged access to the training activities of the SAMBa CDT. More information on the SAMBa CDT may be found here:
This project also involves travelling to Syngenta for engagement and research dissemination.
Anticipated start date: March 2018 or earlier.
Interested applicants are advised to contact Dr Evangelou in the first instance on [Email Address Removed]. When ready to apply, applicants should select the online application form for the PhD programme in Statistics within the Department of Mathematical Sciences. More information on how to apply may be found here:
Applications may close early if a suitable candidate is found; therefore, early application is recommended.
The successful candidate will receive a full studentship including Home/EU tuition fees, a training support fee of £1,000 per annum and a tax-free maintenance allowance of £14,553 per annum (2017/18 rate) for up to 3.5 years.
Note: This studentship is open to UK and EU applicants only; unfortunately, applicants who are classed as Overseas for fee paying purposes are NOT eligible to receive the funding.
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