当前我国空气污染形势日益严峻,空气质量的急剧下降致使人们的身体健康受到严重地危害,同时也妨碍了社会和经济的可持续发展。对PM_(2.5)浓度进行预测,从而监督空气污染状况,防止严重污染的发生受到我国及世界各国人民的广泛关注。因此...当前我国空气污染形势日益严峻,空气质量的急剧下降致使人们的身体健康受到严重地危害,同时也妨碍了社会和经济的可持续发展。对PM_(2.5)浓度进行预测,从而监督空气污染状况,防止严重污染的发生受到我国及世界各国人民的广泛关注。因此提出有效的模型对PM_(2.5)浓度进行准确预测成为时下一个重要问题。本文提出了PLS-M5P(Partial Least Square-M5P)模型用于PM_(2.5)浓度预测。实验结果表明,在空气质量预测方面,与传统的预测模型如BP神经模型相比,PLS-M5P模型树有以下几个优势:(1)能提供直观的数学方程,并能够从获得的数学方程中更深入地理解预测结果。(2)使用PLS-M5P模型生成的树状图可以显示因素的重要性,并且树状图的建立能使决策者更清晰地认识预测过程。(3)建模和预测所用时间很短,而且总是收敛的。(4)预测的精度更高。展开更多
Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their different...Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues.First,a variation of the Relief algorithm,"RFE_Relief algorithm"was proposed to learn the relations between genes and tissue types.Then,a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts.After tissue-specific genes were removed,cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues.The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues,and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.展开更多
文摘当前我国空气污染形势日益严峻,空气质量的急剧下降致使人们的身体健康受到严重地危害,同时也妨碍了社会和经济的可持续发展。对PM_(2.5)浓度进行预测,从而监督空气污染状况,防止严重污染的发生受到我国及世界各国人民的广泛关注。因此提出有效的模型对PM_(2.5)浓度进行准确预测成为时下一个重要问题。本文提出了PLS-M5P(Partial Least Square-M5P)模型用于PM_(2.5)浓度预测。实验结果表明,在空气质量预测方面,与传统的预测模型如BP神经模型相比,PLS-M5P模型树有以下几个优势:(1)能提供直观的数学方程,并能够从获得的数学方程中更深入地理解预测结果。(2)使用PLS-M5P模型生成的树状图可以显示因素的重要性,并且树状图的建立能使决策者更清晰地认识预测过程。(3)建模和预测所用时间很短,而且总是收敛的。(4)预测的精度更高。
基金supported in part by the National Natural Science Foundation of China(Grant No.60234020).
文摘Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues.First,a variation of the Relief algorithm,"RFE_Relief algorithm"was proposed to learn the relations between genes and tissue types.Then,a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts.After tissue-specific genes were removed,cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues.The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues,and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.
