Biological data objects often have both of the following features: (i) they are functions rather than single numbers or vectors, and (ii) they are correlated due to phylogenetic relationships. In this paper we give a flexible statistical model for such data, by combining assumptions from phylogenetics with Gaussian processes. We describe its use as a nonparametric Bayesian prior distribution, both for prediction (placing posterior distributions on ancestral functions) and model selection (comparing rates of evolution across a phylogeny, or identifying the most likely phylogenies consistent with the observed data). Our work is integrative, extending the popular phylogenetic Brownian Motion and Ornstein-Uhlenbeck models to functional data and Bayesian inference, and extending Gaussian Process regression to phylogenies. We provide a brief illustration of the application of our method.
Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution
Virginia Tech（弗吉尼亚理工大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of California, Irvine（加利福尼亚大学欧文分校）；Tulane University（杜兰大学）；Battelle（巴特尔公司）；Duke University（杜克大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；The Ohio State University（俄亥俄州立大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
Evaluating Phylogenetic Comparative Methods under Reticulate Evolutionary Scenarios
Texas A&M University（德克萨斯农工大学）；Lakehead University（湖首大学）；IISD Experimental Lakes Area（国际可持续发展研究所实验湖区）；The Institute for Computational and Experimental Research in Mathematics, Brown University（布朗大学计算与实验数学研究所）；RIKEN iTHEMS（理化学研究所创新智能数学科学中心）
evolSOM: an R Package for evolutionary conservation analysis with SOMs
Universidad Nacional del Litoral（国立滨海大学）；Research Institute for Signals, Systems and Computational Intelligence, sinc(i)（信号、系统与计算智能研究所）；CONICET（国家科学技术研究委员会）
