The Experts below are selected from a list of 57501 Experts worldwide ranked by ideXlab platform
Li Jin - One of the best experts on this subject based on the ideXlab platform.
-
knnauc an open source r package for detecting Nonlinear Dependence between one continuous variable and one binary variable
BMC Bioinformatics, 2018Co-Authors: Xiaoyu Liu, Yi Wang, Weichen Zhou, Meng Hao, Zhenghong Yuan, Jie Liu, Momiao Xiong, Yin Yao Shugart, Jiucun Wang, Li JinAbstract:Background Testing the Dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting Nonlinear Dependence between one continuous variable X and one binary dependent variables Y (0 or 1).
-
bagging nearest neighbor prediction inDependence test an efficient method for Nonlinear Dependence of two continuous variables
Scientific Reports, 2017Co-Authors: Yi Wang, Xiaoyu Liu, Momiao Xiong, Yin Yao Shugart, Jiucun Wang, Xiaofeng Wang, Li JinAbstract:Testing Dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed an efficient method for Nonlinear Dependence of two continuous variables (X and Y). We addressed this research question by using BNNPT (Bagging Nearest-Neighbor Prediction inDependence Test, software available at https://sourceforge.net/projects/bnnpt/). In the BNNPT framework, we first used the value of X to construct a bagging neighborhood structure. We then obtained the out of bag estimator of Y based on the bagging neighborhood structure. The square error was calculated to measure how well Y is predicted by X. Finally, a permutation test was applied to determine the significance of the observed square error. To evaluate the strength of BNNPT compared to seven other methods, we performed extensive simulations to explore the relationship between various methods and compared the false positive rates and statistical power using both simulated and real datasets (Rugao longevity cohort mitochondrial DNA haplogroups and kidney cancer RNA-seq datasets). We concluded that BNNPT is an efficient computational approach to test Nonlinear correlation in real world applications.
-
efficient test for Nonlinear Dependence of two continuous variables
BMC Bioinformatics, 2015Co-Authors: Yi Wang, Momiao Xiong, Yin Yao Shugart, Hongbao Cao, Li JinAbstract:Testing Dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed a new way of testing Nonlinear Dependence between two continuous variables (X and Y). We addressed this research question by using CANOVA (continuous analysis of variance, software available at https://sourceforge.net/projects/canova/ ). In the CANOVA framework, we first defined a neighborhood for each data point related to its X value, and then calculated the variance of the Y value within the neighborhood. Finally, we performed permutations to evaluate the significance of the observed values within the neighborhood variance. To evaluate the strength of CANOVA compared to six other methods, we performed extensive simulations to explore the relationship between methods and compared the false positive rates and statistical power using both simulated and real datasets (kidney cancer RNA-seq dataset). We concluded that CANOVA is an efficient method for testing Nonlinear correlation with several advantages in real data applications.
Jiucun Wang - One of the best experts on this subject based on the ideXlab platform.
-
knnauc an open source r package for detecting Nonlinear Dependence between one continuous variable and one binary variable
BMC Bioinformatics, 2018Co-Authors: Xiaoyu Liu, Yi Wang, Weichen Zhou, Meng Hao, Zhenghong Yuan, Jie Liu, Momiao Xiong, Yin Yao Shugart, Jiucun Wang, Li JinAbstract:Background Testing the Dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting Nonlinear Dependence between one continuous variable X and one binary dependent variables Y (0 or 1).
-
knnauc an open source r package for detecting Nonlinear Dependence between one continuous variable and one binary variable
BMC Bioinformatics, 2018Co-Authors: Xiaoyu Liu, Yi Wang, Weichen Zhou, Meng Hao, Zhenghong Yuan, Jie Liu, Momiao Xiong, Yin Yao Shugart, Jiucun WangAbstract:Testing the Dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting Nonlinear Dependence between one continuous variable X and one binary dependent variables Y (0 or 1). We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/ ). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets). We concluded that knnAUC is an efficient R package to test non-linear Dependence between one continuous variable and one binary dependent variable especially in computational biology area.
-
bagging nearest neighbor prediction inDependence test an efficient method for Nonlinear Dependence of two continuous variables
Scientific Reports, 2017Co-Authors: Yi Wang, Xiaoyu Liu, Momiao Xiong, Yin Yao Shugart, Jiucun Wang, Xiaofeng Wang, Li JinAbstract:Testing Dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed an efficient method for Nonlinear Dependence of two continuous variables (X and Y). We addressed this research question by using BNNPT (Bagging Nearest-Neighbor Prediction inDependence Test, software available at https://sourceforge.net/projects/bnnpt/). In the BNNPT framework, we first used the value of X to construct a bagging neighborhood structure. We then obtained the out of bag estimator of Y based on the bagging neighborhood structure. The square error was calculated to measure how well Y is predicted by X. Finally, a permutation test was applied to determine the significance of the observed square error. To evaluate the strength of BNNPT compared to seven other methods, we performed extensive simulations to explore the relationship between various methods and compared the false positive rates and statistical power using both simulated and real datasets (Rugao longevity cohort mitochondrial DNA haplogroups and kidney cancer RNA-seq datasets). We concluded that BNNPT is an efficient computational approach to test Nonlinear correlation in real world applications.
Xiaoyu Liu - One of the best experts on this subject based on the ideXlab platform.
-
knnauc an open source r package for detecting Nonlinear Dependence between one continuous variable and one binary variable
BMC Bioinformatics, 2018Co-Authors: Xiaoyu Liu, Yi Wang, Weichen Zhou, Meng Hao, Zhenghong Yuan, Jie Liu, Momiao Xiong, Yin Yao Shugart, Jiucun Wang, Li JinAbstract:Background Testing the Dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting Nonlinear Dependence between one continuous variable X and one binary dependent variables Y (0 or 1).
-
knnauc an open source r package for detecting Nonlinear Dependence between one continuous variable and one binary variable
BMC Bioinformatics, 2018Co-Authors: Xiaoyu Liu, Yi Wang, Weichen Zhou, Meng Hao, Zhenghong Yuan, Jie Liu, Momiao Xiong, Yin Yao Shugart, Jiucun WangAbstract:Testing the Dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting Nonlinear Dependence between one continuous variable X and one binary dependent variables Y (0 or 1). We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/ ). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets). We concluded that knnAUC is an efficient R package to test non-linear Dependence between one continuous variable and one binary dependent variable especially in computational biology area.
-
bagging nearest neighbor prediction inDependence test an efficient method for Nonlinear Dependence of two continuous variables
Scientific Reports, 2017Co-Authors: Yi Wang, Xiaoyu Liu, Momiao Xiong, Yin Yao Shugart, Jiucun Wang, Xiaofeng Wang, Li JinAbstract:Testing Dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed an efficient method for Nonlinear Dependence of two continuous variables (X and Y). We addressed this research question by using BNNPT (Bagging Nearest-Neighbor Prediction inDependence Test, software available at https://sourceforge.net/projects/bnnpt/). In the BNNPT framework, we first used the value of X to construct a bagging neighborhood structure. We then obtained the out of bag estimator of Y based on the bagging neighborhood structure. The square error was calculated to measure how well Y is predicted by X. Finally, a permutation test was applied to determine the significance of the observed square error. To evaluate the strength of BNNPT compared to seven other methods, we performed extensive simulations to explore the relationship between various methods and compared the false positive rates and statistical power using both simulated and real datasets (Rugao longevity cohort mitochondrial DNA haplogroups and kidney cancer RNA-seq datasets). We concluded that BNNPT is an efficient computational approach to test Nonlinear correlation in real world applications.
Yi Wang - One of the best experts on this subject based on the ideXlab platform.
-
knnauc an open source r package for detecting Nonlinear Dependence between one continuous variable and one binary variable
BMC Bioinformatics, 2018Co-Authors: Xiaoyu Liu, Yi Wang, Weichen Zhou, Meng Hao, Zhenghong Yuan, Jie Liu, Momiao Xiong, Yin Yao Shugart, Jiucun Wang, Li JinAbstract:Background Testing the Dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting Nonlinear Dependence between one continuous variable X and one binary dependent variables Y (0 or 1).
-
knnauc an open source r package for detecting Nonlinear Dependence between one continuous variable and one binary variable
BMC Bioinformatics, 2018Co-Authors: Xiaoyu Liu, Yi Wang, Weichen Zhou, Meng Hao, Zhenghong Yuan, Jie Liu, Momiao Xiong, Yin Yao Shugart, Jiucun WangAbstract:Testing the Dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting Nonlinear Dependence between one continuous variable X and one binary dependent variables Y (0 or 1). We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/ ). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets). We concluded that knnAUC is an efficient R package to test non-linear Dependence between one continuous variable and one binary dependent variable especially in computational biology area.
-
bagging nearest neighbor prediction inDependence test an efficient method for Nonlinear Dependence of two continuous variables
Scientific Reports, 2017Co-Authors: Yi Wang, Xiaoyu Liu, Momiao Xiong, Yin Yao Shugart, Jiucun Wang, Xiaofeng Wang, Li JinAbstract:Testing Dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed an efficient method for Nonlinear Dependence of two continuous variables (X and Y). We addressed this research question by using BNNPT (Bagging Nearest-Neighbor Prediction inDependence Test, software available at https://sourceforge.net/projects/bnnpt/). In the BNNPT framework, we first used the value of X to construct a bagging neighborhood structure. We then obtained the out of bag estimator of Y based on the bagging neighborhood structure. The square error was calculated to measure how well Y is predicted by X. Finally, a permutation test was applied to determine the significance of the observed square error. To evaluate the strength of BNNPT compared to seven other methods, we performed extensive simulations to explore the relationship between various methods and compared the false positive rates and statistical power using both simulated and real datasets (Rugao longevity cohort mitochondrial DNA haplogroups and kidney cancer RNA-seq datasets). We concluded that BNNPT is an efficient computational approach to test Nonlinear correlation in real world applications.
-
efficient test for Nonlinear Dependence of two continuous variables
BMC Bioinformatics, 2015Co-Authors: Yi Wang, Momiao Xiong, Yin Yao Shugart, Hongbao Cao, Li JinAbstract:Testing Dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed a new way of testing Nonlinear Dependence between two continuous variables (X and Y). We addressed this research question by using CANOVA (continuous analysis of variance, software available at https://sourceforge.net/projects/canova/ ). In the CANOVA framework, we first defined a neighborhood for each data point related to its X value, and then calculated the variance of the Y value within the neighborhood. Finally, we performed permutations to evaluate the significance of the observed values within the neighborhood variance. To evaluate the strength of CANOVA compared to six other methods, we performed extensive simulations to explore the relationship between methods and compared the false positive rates and statistical power using both simulated and real datasets (kidney cancer RNA-seq dataset). We concluded that CANOVA is an efficient method for testing Nonlinear correlation with several advantages in real data applications.
Yin Yao Shugart - One of the best experts on this subject based on the ideXlab platform.
-
knnauc an open source r package for detecting Nonlinear Dependence between one continuous variable and one binary variable
BMC Bioinformatics, 2018Co-Authors: Xiaoyu Liu, Yi Wang, Weichen Zhou, Meng Hao, Zhenghong Yuan, Jie Liu, Momiao Xiong, Yin Yao Shugart, Jiucun Wang, Li JinAbstract:Background Testing the Dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting Nonlinear Dependence between one continuous variable X and one binary dependent variables Y (0 or 1).
-
knnauc an open source r package for detecting Nonlinear Dependence between one continuous variable and one binary variable
BMC Bioinformatics, 2018Co-Authors: Xiaoyu Liu, Yi Wang, Weichen Zhou, Meng Hao, Zhenghong Yuan, Jie Liu, Momiao Xiong, Yin Yao Shugart, Jiucun WangAbstract:Testing the Dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting Nonlinear Dependence between one continuous variable X and one binary dependent variables Y (0 or 1). We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/ ). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets). We concluded that knnAUC is an efficient R package to test non-linear Dependence between one continuous variable and one binary dependent variable especially in computational biology area.
-
bagging nearest neighbor prediction inDependence test an efficient method for Nonlinear Dependence of two continuous variables
Scientific Reports, 2017Co-Authors: Yi Wang, Xiaoyu Liu, Momiao Xiong, Yin Yao Shugart, Jiucun Wang, Xiaofeng Wang, Li JinAbstract:Testing Dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed an efficient method for Nonlinear Dependence of two continuous variables (X and Y). We addressed this research question by using BNNPT (Bagging Nearest-Neighbor Prediction inDependence Test, software available at https://sourceforge.net/projects/bnnpt/). In the BNNPT framework, we first used the value of X to construct a bagging neighborhood structure. We then obtained the out of bag estimator of Y based on the bagging neighborhood structure. The square error was calculated to measure how well Y is predicted by X. Finally, a permutation test was applied to determine the significance of the observed square error. To evaluate the strength of BNNPT compared to seven other methods, we performed extensive simulations to explore the relationship between various methods and compared the false positive rates and statistical power using both simulated and real datasets (Rugao longevity cohort mitochondrial DNA haplogroups and kidney cancer RNA-seq datasets). We concluded that BNNPT is an efficient computational approach to test Nonlinear correlation in real world applications.
-
efficient test for Nonlinear Dependence of two continuous variables
BMC Bioinformatics, 2015Co-Authors: Yi Wang, Momiao Xiong, Yin Yao Shugart, Hongbao Cao, Li JinAbstract:Testing Dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed a new way of testing Nonlinear Dependence between two continuous variables (X and Y). We addressed this research question by using CANOVA (continuous analysis of variance, software available at https://sourceforge.net/projects/canova/ ). In the CANOVA framework, we first defined a neighborhood for each data point related to its X value, and then calculated the variance of the Y value within the neighborhood. Finally, we performed permutations to evaluate the significance of the observed values within the neighborhood variance. To evaluate the strength of CANOVA compared to six other methods, we performed extensive simulations to explore the relationship between methods and compared the false positive rates and statistical power using both simulated and real datasets (kidney cancer RNA-seq dataset). We concluded that CANOVA is an efficient method for testing Nonlinear correlation with several advantages in real data applications.
