Modern statistical techniques for assessing and predicting herbicide performance
Lead Research Organisation:
University of Bath
Department Name: Mathematical Sciences
Abstract
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, I 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. I 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, I 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
The potential benefits are discovery of new and effective herbicides, development or current statistical and machine learning techniques, room for future research and development.
Benefits to the research council include helping UK become the global leading research nation by offering internationally excellent, discovery-driven research, guiding the UK research community in responding to global challenges and government-led initiatives, deliver excellent research with economic and social impact that addresses the needs of the nation.
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, I 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. I 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, I 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
The potential benefits are discovery of new and effective herbicides, development or current statistical and machine learning techniques, room for future research and development.
Benefits to the research council include helping UK become the global leading research nation by offering internationally excellent, discovery-driven research, guiding the UK research community in responding to global challenges and government-led initiatives, deliver excellent research with economic and social impact that addresses the needs of the nation.
People |
ORCID iD |
| Evangelos Evangelou (Primary Supervisor) | |
| Arron GOSNELL (Student) |