Drug-induced liver injury(DILI)is a common adverse drug reaction,which can even result in liver failure[1,2].The Chinese Medical Association issued the Guidelines for the Diagnosis and Treatment of DILI based on the R...Drug-induced liver injury(DILI)is a common adverse drug reaction,which can even result in liver failure[1,2].The Chinese Medical Association issued the Guidelines for the Diagnosis and Treatment of DILI based on the Roussel Uclaf Causality Assessment Method(RUCAM)in 2015[3].A previous study reported that traditional Chinese medicines(TCMs),herbal and dietary supplements,and antituberculosis drugs were the main causes of DILI in China[4].Herb-induced liver injury(HILI)refers to liver injury caused by TCMs,natural drugs,and their related preparations[5].展开更多
Frequent itemset mining serves as the main method of association rule mining.With the limitations in computing space and performance,the association of frequent items in large data mining requires both extensive time ...Frequent itemset mining serves as the main method of association rule mining.With the limitations in computing space and performance,the association of frequent items in large data mining requires both extensive time and effort,particularly when the datasets become increasingly larger.In the process of associated data mining in a big data environment,the MapReduce programming model is typically used to perform task partitioning and parallel processing,which could improve the execution effciency of the algorithm.However,to ensure that the associated rule is not destroyed during task partitioning and parallel processing,the inner-relationship data must be stored in the computer space.Because inner-relationship data are redundant,storage of these data will significantly increase the space usage in comparison with the original dataset.In this study,we find that the formation of the frequent pattern(FP)mining algorithm depends mainly on the conditional pattern bases.Based on the parallel frequent pattern(PFP)algorithm theory,the grouping model divides frequent items into several groups according to their frequencies.We propose a non-group PFP(NG-PFP)mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks.Moreover,we present the NG-PFP algorithm for task partition and parallel processing,and its performance in the Hadoop cluster environment is analyzed and discussed.Experimental results indicate that the non-group model shows obvious improvement in terms of computational effciency and the space utilization rate.展开更多
基金This study was supported by grants from Zhejiang Health Science and Technology Project(2022ZA052)Zhejiang Provincial Natural Science Foundation of China(LY23H030001).
文摘Drug-induced liver injury(DILI)is a common adverse drug reaction,which can even result in liver failure[1,2].The Chinese Medical Association issued the Guidelines for the Diagnosis and Treatment of DILI based on the Roussel Uclaf Causality Assessment Method(RUCAM)in 2015[3].A previous study reported that traditional Chinese medicines(TCMs),herbal and dietary supplements,and antituberculosis drugs were the main causes of DILI in China[4].Herb-induced liver injury(HILI)refers to liver injury caused by TCMs,natural drugs,and their related preparations[5].
基金project supported by the Fundamental Research Funds for the Central Universities,China(No.2412015KJ005)the Twelfth Five-Year Plan of the Education Department of Jilin Province,China(No.557)the Thirteenth Five-Year Plan for Scientific Research of the Education Department of Jilin Province,China(No.JJKH20191197KJ)
文摘Frequent itemset mining serves as the main method of association rule mining.With the limitations in computing space and performance,the association of frequent items in large data mining requires both extensive time and effort,particularly when the datasets become increasingly larger.In the process of associated data mining in a big data environment,the MapReduce programming model is typically used to perform task partitioning and parallel processing,which could improve the execution effciency of the algorithm.However,to ensure that the associated rule is not destroyed during task partitioning and parallel processing,the inner-relationship data must be stored in the computer space.Because inner-relationship data are redundant,storage of these data will significantly increase the space usage in comparison with the original dataset.In this study,we find that the formation of the frequent pattern(FP)mining algorithm depends mainly on the conditional pattern bases.Based on the parallel frequent pattern(PFP)algorithm theory,the grouping model divides frequent items into several groups according to their frequencies.We propose a non-group PFP(NG-PFP)mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks.Moreover,we present the NG-PFP algorithm for task partition and parallel processing,and its performance in the Hadoop cluster environment is analyzed and discussed.Experimental results indicate that the non-group model shows obvious improvement in terms of computational effciency and the space utilization rate.
