Next generation phylogenetic modelling using machine learning

使用机器学习的下一代系统发育建模

• 批准号: 402442-2011
• 负责人: BouchardCôté, Alexandre
• 金额: $ 2.11万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=508288
• 关键词: Next; generation; phylogenetic; modelling; using

The goal of phylogenetics is to draw inferences about the past from the diversity of the present. Although phylogenetics is best known from its application to the reconstruction of biological histories (from biodiversity), phylogenetic also has ramifications for the problem of reconstructing linguistic histories (from the world's linguistic diversity). The program I propose involves both biological and linguistic reconstructions. I hypothesize that the two problems have sufficient similarities to justify a joint study, but also have sufficient differences to foster innovation.

系统发育学的目标是从现在的多样性中推断过去。虽然系统发育学以其应用于生物历史（从生物多样性）的重建而闻名，但系统发育学也对重建语言历史（从世界语言多样性）的问题产生了影响。我提出的方案包括生物学和语言学的重建。我假设这两个问题有足够的相似性来证明联合研究的合理性，但也有足够的差异来促进创新。