One view of finding a personalized solution of reduct in an information system is grounded on the viewpoint that attribute order can serve as a kind of semantic representation of user requirements. Thus the problem of...One view of finding a personalized solution of reduct in an information system is grounded on the viewpoint that attribute order can serve as a kind of semantic representation of user requirements. Thus the problem of finding personalized solutions can be transformed into computing the reduct on an attribute order. The second attribute theorem describes the relationship between the set of attribute orders and the set of reducts, and can be used to transform the problem of searching solutions to meet user requirements into the problem of modifying reduct based on a given attribute order. An algorithm is implied based on the second attribute theorem, with computation on the discernibility matrix. Its time complexity is O（n^2 × m） （n is the number of the objects and m the number of the attributes of an information system）. This paper presents another effective second attribute algorithm for facilitating the use of the second attribute theorem, with computation on the tree expression of an information system. The time complexity of the new algorithm is linear in n. This algorithm is proved to be equivalent to the algorithm on the discernibility matrix.展开更多
基于知识的工程(Knowledge Based Engineering,KBE)技术为楔横轧模具设计知识的整理、收集与重用提供了手段。本文将楔横轧轧件作为对象,采用特征表达的方法来描述轧件。建立了轧件特征的提取原则,通过对轧件的材料特征、坯料特征、主...基于知识的工程(Knowledge Based Engineering,KBE)技术为楔横轧模具设计知识的整理、收集与重用提供了手段。本文将楔横轧轧件作为对象,采用特征表达的方法来描述轧件。建立了轧件特征的提取原则,通过对轧件的材料特征、坯料特征、主形特征3个特征类的分析,得出了楔横轧轧件特征的描述方法。重点分析了对楔横轧工艺影响较大的特征属性。采用特征表达法简单、完整的描述了轧件,简化了楔横轧知识运用的步骤以及内容,为基于KBE的楔横轧模具设计提供了知识基础。展开更多
Background:Accumulating evidence shows that long non-coding RNAs(lncRNAs)play critical roles in cancer progression.The possible association between lncRNAs and herbal medicine is yet to be known.This study aims to ide...Background:Accumulating evidence shows that long non-coding RNAs(lncRNAs)play critical roles in cancer progression.The possible association between lncRNAs and herbal medicine is yet to be known.This study aims to identify medicinal herbs associated with lncRNAs by RNA-seq data for breast and prostate cancer.Methods:To develop the optimal approach for identifying cancer-related lncRNAs,we implemented two steps:(1)applying protein–protein interaction(PPI),Gene Ontology(GO),and pathway analyses,and(2)applying attribute weighting and finding the efficient classification model of the machine learning approach.Results:In the first step,GO terms and pathway analyses on differential co-expressed mRNAs revealed that lncRNAs were widely co-expressed with metabolic process genes.We identified two hub lncRNA-mRNA networks that implicate lncRNAs associated with breast and prostate cancer.In the second step,we implemented various machine learning-based prediction systems(Decision Tree,Random Forest,Deep Learning,and Gradient-Boosted Tree)on the non-transformed and Z-standardized differential co-expressed lncRNAs.Based on five-fold cross-validation,we obtained high accuracy(91.11%),high sensitivity(88.33%),and high specificity(93.33%)in Deep Learning which reinforces the biomarker power of identified lncRNAs in this study.As data originally came from different cell lines at different durations of herbal treatment intervention,we applied seven attribute weighting algorithms to check the effects of variables on identifying lncRNAs.Attribute weighting results showed that the cell line and time had little or no effect on the selected lncRNAs list.Besides,we identified one known lncRNAs,downregulated RNA in cancer(DRAIC),as an essential feature.Conclusions:This study will provide further insights to investigate the potential therapeutic and prognostic targets for prostate cancer(PC)and breast cancer(BC)in common.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No. 60175023 and the National Basic Research 973 Program of China under Grant No. 2004CB318103
文摘One view of finding a personalized solution of reduct in an information system is grounded on the viewpoint that attribute order can serve as a kind of semantic representation of user requirements. Thus the problem of finding personalized solutions can be transformed into computing the reduct on an attribute order. The second attribute theorem describes the relationship between the set of attribute orders and the set of reducts, and can be used to transform the problem of searching solutions to meet user requirements into the problem of modifying reduct based on a given attribute order. An algorithm is implied based on the second attribute theorem, with computation on the discernibility matrix. Its time complexity is O（n^2 × m） （n is the number of the objects and m the number of the attributes of an information system）. This paper presents another effective second attribute algorithm for facilitating the use of the second attribute theorem, with computation on the tree expression of an information system. The time complexity of the new algorithm is linear in n. This algorithm is proved to be equivalent to the algorithm on the discernibility matrix.
文摘基于知识的工程(Knowledge Based Engineering,KBE)技术为楔横轧模具设计知识的整理、收集与重用提供了手段。本文将楔横轧轧件作为对象,采用特征表达的方法来描述轧件。建立了轧件特征的提取原则,通过对轧件的材料特征、坯料特征、主形特征3个特征类的分析,得出了楔横轧轧件特征的描述方法。重点分析了对楔横轧工艺影响较大的特征属性。采用特征表达法简单、完整的描述了轧件,简化了楔横轧知识运用的步骤以及内容,为基于KBE的楔横轧模具设计提供了知识基础。
文摘Background:Accumulating evidence shows that long non-coding RNAs(lncRNAs)play critical roles in cancer progression.The possible association between lncRNAs and herbal medicine is yet to be known.This study aims to identify medicinal herbs associated with lncRNAs by RNA-seq data for breast and prostate cancer.Methods:To develop the optimal approach for identifying cancer-related lncRNAs,we implemented two steps:(1)applying protein–protein interaction(PPI),Gene Ontology(GO),and pathway analyses,and(2)applying attribute weighting and finding the efficient classification model of the machine learning approach.Results:In the first step,GO terms and pathway analyses on differential co-expressed mRNAs revealed that lncRNAs were widely co-expressed with metabolic process genes.We identified two hub lncRNA-mRNA networks that implicate lncRNAs associated with breast and prostate cancer.In the second step,we implemented various machine learning-based prediction systems(Decision Tree,Random Forest,Deep Learning,and Gradient-Boosted Tree)on the non-transformed and Z-standardized differential co-expressed lncRNAs.Based on five-fold cross-validation,we obtained high accuracy(91.11%),high sensitivity(88.33%),and high specificity(93.33%)in Deep Learning which reinforces the biomarker power of identified lncRNAs in this study.As data originally came from different cell lines at different durations of herbal treatment intervention,we applied seven attribute weighting algorithms to check the effects of variables on identifying lncRNAs.Attribute weighting results showed that the cell line and time had little or no effect on the selected lncRNAs list.Besides,we identified one known lncRNAs,downregulated RNA in cancer(DRAIC),as an essential feature.Conclusions:This study will provide further insights to investigate the potential therapeutic and prognostic targets for prostate cancer(PC)and breast cancer(BC)in common.
