CDI Type I: Collaborative Research: Machine Learning in Taxonomic Research

CDI I 型：协作研究：分类学研究中的机器学习

• 批准号: 1027989
• 负责人: Yixin Chen
• 金额: $ 28.55万
• 依托单位: University of Mississippi
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1027989&HistoricalAwards=false
• 关键词: CDI; Type; Collaborative; Research; Machine

Intellectual MeritIt is estimated that less than 10 percent of the world's species have been described, yet species are being lost daily due to human destruction of natural habitats. Considering the fast pace of habitat destruction, experts fear that many species will become extinct before they can be discovered and formally described. The job of describing the earth's remaining species is exacerbated by the shrinking number of practicing taxonomists and the very slow pace of traditional taxonomic research. In describing new species of animals, taxonomists typically rely on specimens deposited in natural history museums. They have to make careful counts and measurements on large numbers of specimens from multiple populations across the geographic ranges of both known and newly discovered species, in order to diagnose the new species as distinct from all of its known relatives. The process is laborious and can take years or even decades to complete, depending on the geographic range of the species. In this project, the research team will develop new machine learning methods for taxonomic research, with the specific aim of fundamentally increasing the pace of taxonomic revision.The scientific research will focus on two areas: species identification and new species discovery. In distinguishing a species from others, taxonomists must identify a set of diagnostic characters that distinguishes the species in question from all of its known relatives. To automate and expedite this laborious process, the team will explore existing feature subset selection techniques, and will develop new ones. Images of categorized specimens will be used to train a collection of statistical models representing the known taxonomic grouping of organisms. An "optimal" set of body shape characters will be automatically identified. New species discovery is the most important research objective in taxonomy. From a machine learning point of view, detecting new species is fundamentally different from the problem of recognizing known species because by definition, the training set does not contain prior knowledge of a new species. The research team will formulate new species discovery as a novelty detection problem, and will develop an efficient novelty detection framework for taxonomic tasks.Broader ImpactThe project, if carried out successfully, will demonstrate the fruitfulness of fusing technologies from different fields. From the biology side, the project will demonstrate that machine learning techniques can assist taxonomists and evolutionary biologists in various research tasks, hence fundamentally accelerate the pace of taxonomic revision. From the computer science side, researchers will benefit from the computational challenges motivated from real-world biological problems and the database created in the project. New machine learning algorithms will be developed to impact taxonomic research. The PIs will integrate the research with their educational activities at both University of Mississippi and Tulane University. The PIs will devote additional efforts to mentoring female and underrepresented minority students involved in exploring cutting-edge interdisciplinary research.

据估计，世界上被描述的物种不到10%，然而由于人类对自然栖息地的破坏，物种每天都在消失。考虑到栖息地遭到破坏的速度之快，专家们担心，许多物种在被发现和正式描述之前就会灭绝。由于职业分类学家数量的减少和传统分类研究的缓慢步伐，描述地球上现存物种的工作变得更加困难。在描述新的动物物种时，分类学家通常依靠存放在自然历史博物馆的标本。他们必须对跨越地理范围的多个种群的大量标本进行仔细的计数和测量，包括已知的和新发现的物种，以便诊断新物种与所有已知的近亲不同。这个过程很辛苦，可能需要几年甚至几十年才能完成，这取决于物种的地理范围。在这个项目中，研究团队将开发新的机器学习方法用于分类学研究，其具体目标是从根本上加快分类学修订的速度。科学研究将集中在两个方面：物种鉴定和新物种发现。为了将一个物种与其他物种区分开来，分类学家必须确定一套诊断特征，以将该物种与所有已知的近亲区分开来。为了自动化和加速这个费力的过程，团队将探索现有的特征子集选择技术，并将开发新的技术。分类标本的图像将用于训练代表已知生物分类分组的统计模型集合。一组“最优”的体型字符将被自动识别。新物种的发现是分类学中最重要的研究目标。从机器学习的角度来看，检测新物种与识别已知物种的问题有着根本的不同，因为根据定义，训练集不包含新物种的先验知识。研究团队将把新物种发现作为一个新颖性检测问题，并将为分类学任务开发一个有效的新颖性检测框架。更广泛的影响该项目如果成功实施，将展示融合不同领域技术的丰硕成果。从生物学方面来看，该项目将证明机器学习技术可以帮助分类学家和进化生物学家完成各种研究任务，从而从根本上加快分类修订的步伐。在计算机科学方面，研究人员将受益于由现实世界的生物问题和项目中创建的数据库所激发的计算挑战。将开发新的机器学习算法来影响分类学研究。pi将把研究与他们在密西西比大学和杜兰大学的教育活动结合起来。pi将投入更多的努力，指导女性和少数族裔学生参与探索前沿的跨学科研究。