Modern statistical techniques for assessing and predicting herbicide performance

用于评估和预测除草剂性能的现代统计技术

• 批准号: 1994216
• 金额: --
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1994216
• 关键词: Modern; statistical; techniques; assessing; predicting

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 performanceThe 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.

该项目将为图形数据开发现代回归方法。随着用于化学测试的筛选工具的激增，现在可以很容易地创建大量的化学物质数据库。另一方面，开发可用于分析这些大型数据库的严格统计方法还处于初级阶段，必须进一步开发以促进化学物质的发现。在这个项目中，我将开发统计模型和方法，从化合物的描述特征和筛选测试中的表现来评估化合物，并相应地计算每种化学品的定量分数。我将应用这种方法来解决先正达提供的实际问题，使用他们在除草剂筛选实验中的数据。通常，数以千计的潜在除草剂将在实验室接受一系列筛选测试(化验测试)，每次无效的化合物都将被丢弃，其余的将根据一套更复杂的标准进行评估，最后几个将接受严格的田间试验。显然，早期试验的数据将显示出高度的不确定性和主观性。已经与先正达一起确定了该项目不同可能开发的动机和起点。在项目的开始阶段，我将开发一个模型，使用剂量、测试的植物种类和化学物质的结构等信息来预测除草剂在每次测试中的性能，这些信息可以用图表表示。将探索现代回归方法，如支持向量回归、神经网络和高斯过程回归。该模型应该利用植物物种和化学物质家族之间的关系来提高预测性能。潜在的好处是发现新的有效除草剂，开发或当前的统计和机器学习技术，未来研究和发展的空间。研究理事会的好处包括通过提供国际优秀的、发现驱动的研究，帮助英国成为全球领先的研究国家，指导英国研究界应对全球挑战和政府主导的倡议，提供具有经济和社会影响的优秀研究，满足国家需求。