Composition vector trees （CVTrees） are inferred from whole-genome data by an alignment-free and parameter-free method. The agreement of these trees with the corresponding taxonomy provides an objective justification...Composition vector trees （CVTrees） are inferred from whole-genome data by an alignment-free and parameter-free method. The agreement of these trees with the corresponding taxonomy provides an objective justification of the inferred phylogeny. In this work, we show the stability and self-consistency of CVTrees by performing bootstrap and jackknife re-sampling tests adapted to this alignment-free approach. Our ultimate goal is to advocate the viewpoint that time-consuming statistical re-sampling tests can be avoided at all in using this alignment-free approach. Agreement with taxonomy should be taken as a major criterion to estimate prokaryotic phylogenetic trees.展开更多
The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function netw...The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging(f MRI)network. Finally, the result shows that the best model only is based on anatomical distance.展开更多
基金supported by the National Basic Research Program of China (the 973 Program, Grant No. 2007CB814800)the Shanghai Leading Academic Discipline Project (Grant No. B111)
文摘Composition vector trees （CVTrees） are inferred from whole-genome data by an alignment-free and parameter-free method. The agreement of these trees with the corresponding taxonomy provides an objective justification of the inferred phylogeny. In this work, we show the stability and self-consistency of CVTrees by performing bootstrap and jackknife re-sampling tests adapted to this alignment-free approach. Our ultimate goal is to advocate the viewpoint that time-consuming statistical re-sampling tests can be avoided at all in using this alignment-free approach. Agreement with taxonomy should be taken as a major criterion to estimate prokaryotic phylogenetic trees.
基金the National Natural Science Foundation of China(Nos.6117013661373101+3 种基金61472270 and61402318)the Natural Science Foundation of Shanxi(No.2014021022-5)the Special/Youth Foundation of Taiyuan University of Technology(No.2012L014)the Youth Team Fund of Taiyuan University of Technology(Nos.2013T047 and 2013T048)
文摘The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging(f MRI)network. Finally, the result shows that the best model only is based on anatomical distance.
