Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was ...Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods.The dataset was examined with six widely accepted indices which are:the maximum tangential stress around the excavation boundary(MTS),uniaxial compressive strength(UCS)and uniaxial tensile strength(UTS)of the intact rock,stress concentration factor(SCF),rock brittleness index(BI),and strain energy storage index(EEI).Two boosting(AdaBoost.M1,SAMME)and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated.The available dataset was randomly divided into training set(2/3 of whole datasets)and testing set(the remaining datasets).Repeated 10-fold cross validation(CV)was applied as the validation method for tuning the hyper-parameters.The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles.According to 10-fold CV,the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1,SAMME algorithms and empirical criteria methods.展开更多
When detecting deletions in complex human genomes,split-read approaches using short reads generated with next-generation sequencing still face the challenge that either false discovery rate is high,or sensitivity is l...When detecting deletions in complex human genomes,split-read approaches using short reads generated with next-generation sequencing still face the challenge that either false discovery rate is high,or sensitivity is low.To address the problem,an integrated strategy is proposed.It organically combines the fundamental theories of the three mainstream methods(read-pair approaches,split-read technologies and read-depth analysis) with modern machine learning algorithms,using the recipe of feature extraction as a bridge.Compared with the state-of-art split-read methods for deletion detection in both low and high sequence coverage,the machine-learning-aided strategy shows great ability in intelligently balancing sensitivity and false discovery rate and getting a both more sensitive and more precise call set at single-base-pair resolution.Thus,users do not need to rely on former experience to make an unnecessary trade-off beforehand and adjust parameters over and over again any more.It should be noted that modern machine learning models can play an important role in the field of structural variation prediction.展开更多
基金Projects(41807259,51604109)supported by the National Natural Science Foundation of ChinaProject(2020CX040)supported by the Innovation-Driven Project of Central South University,ChinaProject(2018JJ3693)supported by the Natural Science Foundation of Hunan Province,China。
文摘Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods.The dataset was examined with six widely accepted indices which are:the maximum tangential stress around the excavation boundary(MTS),uniaxial compressive strength(UCS)and uniaxial tensile strength(UTS)of the intact rock,stress concentration factor(SCF),rock brittleness index(BI),and strain energy storage index(EEI).Two boosting(AdaBoost.M1,SAMME)and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated.The available dataset was randomly divided into training set(2/3 of whole datasets)and testing set(the remaining datasets).Repeated 10-fold cross validation(CV)was applied as the validation method for tuning the hyper-parameters.The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles.According to 10-fold CV,the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1,SAMME algorithms and empirical criteria methods.
基金Project(61472026)supported by the National Natural Science Foundation of ChinaProject(2014J410081)supported by Guangzhou Scientific Research Program,China
文摘When detecting deletions in complex human genomes,split-read approaches using short reads generated with next-generation sequencing still face the challenge that either false discovery rate is high,or sensitivity is low.To address the problem,an integrated strategy is proposed.It organically combines the fundamental theories of the three mainstream methods(read-pair approaches,split-read technologies and read-depth analysis) with modern machine learning algorithms,using the recipe of feature extraction as a bridge.Compared with the state-of-art split-read methods for deletion detection in both low and high sequence coverage,the machine-learning-aided strategy shows great ability in intelligently balancing sensitivity and false discovery rate and getting a both more sensitive and more precise call set at single-base-pair resolution.Thus,users do not need to rely on former experience to make an unnecessary trade-off beforehand and adjust parameters over and over again any more.It should be noted that modern machine learning models can play an important role in the field of structural variation prediction.
