目的将整合通路信息的sparse group LASSO方法与近年来发表的表型预测方法进行比较,通过模拟各种复杂疾病可能的遗传结构,比较各方法的预测能力,期望通过TCGA数据找到高效和稳健的统计方法。方法本研究利用SGL方法整合基因途径信息和基...目的将整合通路信息的sparse group LASSO方法与近年来发表的表型预测方法进行比较,通过模拟各种复杂疾病可能的遗传结构,比较各方法的预测能力,期望通过TCGA数据找到高效和稳健的统计方法。方法本研究利用SGL方法整合基因途径信息和基因表达数据,并与传统模型(LASSO、Enet、GSSLASSO)进行比较。通过乳腺癌真实基因型数据模拟表型数据:考虑不同分组(分组k=50,200,300,328)和不同遗传度对模型的影响(遗传度h 2=0.3,0.5,0.8)。采用相关系数R评价几种模型的预测能力,进一步通过结直肠癌(CRC)、胰腺癌(PAAD)、乳腺癌(BRCA)三个真实数据比较各方法表型预测的准确性。结果模拟结果表明,随着遗传度的增高,各方法的预测准确性也逐渐增高。整合通路信息的SGL方法和GSSLASSO方法比传统的LASSO和Enet方法有着更高的预测精度。而两种整合通路信息的方法中,SGL方法有着更好的预测能力和稳定性。在50,200,300分组情况下,GSSLASSO预测效果和LASSO以及Enet相近,但是在考虑通路信息的328分组下,GSSLASSO表现出了较好的预测效果。实例数据分析CRC,PAAD数据中,SGL方法具有最优的预测精度,其次是GSSLASSO,LASSO和Enet方法预测效果最差。结论整合通路信息的预测方法预测效果明显优于一般模型,而无论是在模拟数据还是实例数据中SGL的方法具有最优的预测精度。展开更多
A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal gen...A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal generation gap (MGP) and blend crossover operators (BLX α). Compared with traditional GA implemented in binary number, the processing time of the improved GA is faster because coding and decoding are unnecessary. In addition, it needn t set parameters such as the probability value of crossove...展开更多
A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence da...A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.展开更多
文摘目的将整合通路信息的sparse group LASSO方法与近年来发表的表型预测方法进行比较,通过模拟各种复杂疾病可能的遗传结构,比较各方法的预测能力,期望通过TCGA数据找到高效和稳健的统计方法。方法本研究利用SGL方法整合基因途径信息和基因表达数据,并与传统模型(LASSO、Enet、GSSLASSO)进行比较。通过乳腺癌真实基因型数据模拟表型数据:考虑不同分组(分组k=50,200,300,328)和不同遗传度对模型的影响(遗传度h 2=0.3,0.5,0.8)。采用相关系数R评价几种模型的预测能力,进一步通过结直肠癌(CRC)、胰腺癌(PAAD)、乳腺癌(BRCA)三个真实数据比较各方法表型预测的准确性。结果模拟结果表明,随着遗传度的增高,各方法的预测准确性也逐渐增高。整合通路信息的SGL方法和GSSLASSO方法比传统的LASSO和Enet方法有着更高的预测精度。而两种整合通路信息的方法中,SGL方法有着更好的预测能力和稳定性。在50,200,300分组情况下,GSSLASSO预测效果和LASSO以及Enet相近,但是在考虑通路信息的328分组下,GSSLASSO表现出了较好的预测效果。实例数据分析CRC,PAAD数据中,SGL方法具有最优的预测精度,其次是GSSLASSO,LASSO和Enet方法预测效果最差。结论整合通路信息的预测方法预测效果明显优于一般模型,而无论是在模拟数据还是实例数据中SGL的方法具有最优的预测精度。
文摘A novel real coded improved genetic algorithm (GA) of training feed forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation alternation model based the minimal generation gap (MGP) and blend crossover operators (BLX α). Compared with traditional GA implemented in binary number, the processing time of the improved GA is faster because coding and decoding are unnecessary. In addition, it needn t set parameters such as the probability value of crossove...
文摘A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.
