Large high-dimensional data have posed great challenges to existing algorithms for frequent itemsets mining.To solve the problem,a hybrid method,consisting of a novel row enumeration algorithm and a column enumeration...Large high-dimensional data have posed great challenges to existing algorithms for frequent itemsets mining.To solve the problem,a hybrid method,consisting of a novel row enumeration algorithm and a column enumeration algorithm,is proposed.The intention of the hybrid method is to decompose the mining task into two subtasks and then choose appropriate algorithms to solve them respectively.The novel algorithm,i.e.,Inter-transaction is based on the characteristic that there are few common items between or among long transactions.In addition,an optimization technique is adopted to improve the performance of the intersection of bit-vectors.Experiments on synthetic data show that our method achieves high performance in large high-dimensional data.展开更多
The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier prediction.Regrettably,due to...The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier prediction.Regrettably,due to the highdimensionality nature of neural data and the least available samples,modelling an efficient computer diagnostic system is highly solicited.Learning approaches,specifically deep learning approaches,are essential in disease prediction.Deep Learning(DL)approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging.A novel 3D-Convolutional Neural Network(3D-CNN)architecture is proposed to predict AD with Magnetic resonance imaging(MRI)data.The proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary classification.The proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative(ADNI)data.The outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general approaches.The predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.Keras’experimentation is carried out,and the model’s superiority is compared with various advanced approaches for multi-level classification.The proposed model gives better prediction accuracy,precision,recall,and F-measure than other systems like Long Short Term Memory-Recurrent Neural Networks(LSTM-RNN),Stacked Autoencoder with Deep Neural Networks(SAE-DNN),Deep Convolutional Neural Networks(D-CNN),Two Dimensional Convolutional Neural Networks(2D-CNN),Inception-V4,ResNet,and Two Dimensional Convolutional Neural Networks(3D-CNN).展开更多
Quantile regression links the whole distribution of an outcome to the covariates of interest and has become an important alternative to commonly used regression models.However,the presence of censored data such as sur...Quantile regression links the whole distribution of an outcome to the covariates of interest and has become an important alternative to commonly used regression models.However,the presence of censored data such as survival time,often the main endpoint in cancer studies,has hampered the use of quantile regression techniques because of the incompleteness of data.With the advent of the precision medicine era and availability of high throughput data,quantile regression with high-dimensional predictors has attracted much attention and provided added insight compared to traditional regression approaches.This paper provides a practical guide for using quantile regression for right censored outcome data with covariates of low-or highdimensionality.We frame our discussion using a dataset from the Boston Lung Cancer Survivor Cohort,a hospital-based prospective cohort study,with the goals of broadening the scope of cancer research,maximizing the utility of collected data,and offering useful statistical alternatives.We use quantile regression to identify clinical and molecular predictors,for example CpG methylation sites,associated with high-risk lung cancer patients,for example those with short survival.展开更多
基金The work was supported in part by Research Fund for the Doctoral Program of Higher Education of China(No.20060255006)
文摘Large high-dimensional data have posed great challenges to existing algorithms for frequent itemsets mining.To solve the problem,a hybrid method,consisting of a novel row enumeration algorithm and a column enumeration algorithm,is proposed.The intention of the hybrid method is to decompose the mining task into two subtasks and then choose appropriate algorithms to solve them respectively.The novel algorithm,i.e.,Inter-transaction is based on the characteristic that there are few common items between or among long transactions.In addition,an optimization technique is adopted to improve the performance of the intersection of bit-vectors.Experiments on synthetic data show that our method achieves high performance in large high-dimensional data.
文摘The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier prediction.Regrettably,due to the highdimensionality nature of neural data and the least available samples,modelling an efficient computer diagnostic system is highly solicited.Learning approaches,specifically deep learning approaches,are essential in disease prediction.Deep Learning(DL)approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging.A novel 3D-Convolutional Neural Network(3D-CNN)architecture is proposed to predict AD with Magnetic resonance imaging(MRI)data.The proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary classification.The proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative(ADNI)data.The outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general approaches.The predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.Keras’experimentation is carried out,and the model’s superiority is compared with various advanced approaches for multi-level classification.The proposed model gives better prediction accuracy,precision,recall,and F-measure than other systems like Long Short Term Memory-Recurrent Neural Networks(LSTM-RNN),Stacked Autoencoder with Deep Neural Networks(SAE-DNN),Deep Convolutional Neural Networks(D-CNN),Two Dimensional Convolutional Neural Networks(2D-CNN),Inception-V4,ResNet,and Two Dimensional Convolutional Neural Networks(3D-CNN).
基金This study was supported by the National Institute of Health(U01CA209414).
文摘Quantile regression links the whole distribution of an outcome to the covariates of interest and has become an important alternative to commonly used regression models.However,the presence of censored data such as survival time,often the main endpoint in cancer studies,has hampered the use of quantile regression techniques because of the incompleteness of data.With the advent of the precision medicine era and availability of high throughput data,quantile regression with high-dimensional predictors has attracted much attention and provided added insight compared to traditional regression approaches.This paper provides a practical guide for using quantile regression for right censored outcome data with covariates of low-or highdimensionality.We frame our discussion using a dataset from the Boston Lung Cancer Survivor Cohort,a hospital-based prospective cohort study,with the goals of broadening the scope of cancer research,maximizing the utility of collected data,and offering useful statistical alternatives.We use quantile regression to identify clinical and molecular predictors,for example CpG methylation sites,associated with high-risk lung cancer patients,for example those with short survival.
