Collaborative Research: AVATOL - Next Generation Phenomics for the Tree of Life

合作研究：AVATOL - 生命之树的下一代表型组学

• 批准号: 1208310
• 负责人: Robert Thacker
• 金额: $ 7.91万
• 依托单位: University of Alabama at Birmingham
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208310&HistoricalAwards=false
• 关键词: Collaborative; Research; AVATOL; Next; Generation

Darwin's theory of evolution explained that millions of species are related, and dealt biologists and paleontologists the enormous challenge of discovering the branching pattern of the Tree of Life. Work on this great challenge is producing a map of species-relatedness through Earth history, and answering questions such as "what is the closest relative of a species?" and "what species make important products?" To do this scientists draw on all heritable features, including genotypes (e.g., DNA) and phenotypes (e.g., anatomy). Studying phenotypes, however, has remained complicated and slow, because it has not been revolutionized by computer science and engineering innovations. A team of biologists, computer scientists, and paleontologists will extend and adapt methods from computer vision, machine learning, crowd-sourcing and natural language processing to enable rapid and automated study of phenotypes for the Tree of Life on a vast scale. The three-year goal is to release large phenomic datasets built using new methods, and to provide the public and scientific community with tools for future work. Planned is the training of teachers and students (kindergarten - postdoctoral levels) and the engagement of "citizen scien-tists." Enormous phenomic datasets, many with images, will fill an important public interest in biodiversity and the fossil record.

达尔文的进化论解释说，数以百万计的物种是相关的，而处理生物学家和古生物学家发现了生命之树的分支模式的巨大挑战。在这一巨大挑战上的工作是在地球历史上产生与物种相关性的地图，并回答诸如“物种最亲密的亲戚是什么？”之类的问题。和“哪种物种生产重要的产品？”要做这些科学家，借鉴了所有可遗传的特征，包括基因型（例如DNA）和表型（例如解剖学）。但是，研究表型仍然变得复杂且缓慢，因为它并未通过计算机科学和工程创新彻底改变。 一个生物学家，计算机科学家和古生物学家团队将从计算机视觉，机器学习，众包和自然语言处理中扩展和适应方法，以实现对生命树的表型快速，自动化的研究。为期三年的目标是释放使用新方法构建的大型现象数据集，并为公众和科学界提供未来工作的工具。计划的是对教师和学生的培训（幼儿园 - 博士后一级）和“公民科学人员”的参与。许多带有图像的现象数据集将填补对生物多样性和化石记录的重要公众兴趣。