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| 1 | Metabolite-Disease Association Prediction Algorithm Combining DeepWalk and Random Forest显示文摘Identifying the association between metabolites and diseases will help us understand the pathogenesis of diseases,which has great significance in diagnosing and treating diseases.However,traditional biometric methods are time consuming and expensive.Accordingly,we propose a new metabolite-disease association prediction algorithm based on DeepWalk and random forest(DWRF),which consists of the following key steps:First,the semantic similarity and information entropy similarity of diseases are integrated as the final disease similarity.Similarly,molecular fingerprint similarity and information entropy similarity of metabolites are integrated as the final metabolite similarity.Then,DeepWalk is used to extract metabolite features based on the network of metabolite-gene associations.Finally,a random forest algorithm is employed to infer metabolite-disease associations.The experimental results show that DWRF has good performances in terms of the area under the curve value,leave-one-out cross-validation,and five-fold cross-validation.Case studies also indicate that DWRF has a reliable performance in metabolite-disease association prediction. | Jiaojiao Tie Xiujuan Lei Yi Pan | 2022 | Tsinghua Science and Technology2022，27，1： | 1 |
| 2 | 基于多核SVM的AdaBoost心力衰竭死亡率评估模型显示文摘【目的】心力衰竭简称心衰,是一种复杂的临床综合征,具有高发病率、高死亡率和预后效果不佳等显著特点,是各类心脏疾病发展的终末期,严重危害人类健康。因此,对心衰患者进行早期的预后评估研究至关重要,可以最大程度地帮助患者生存。【方法】提出一种基于多核支持向量机(multi kernel support vector machine,MK-SVM)和自适应提升算法(adaptive boosting,AdaBoost)的心力衰竭死亡率评估模型(MK-SVM-AdaBoost).该算法利用MK-SVM将特征映射到高维空间,并依据AdaBoost算法将基本分类器进行集成,实现死亡率的精确预测。同时,将合成少数过采样技术(synthetic minority oversampling technique,SMOTE)和Tomek links欠采样技术相结合的混合抽样方法引入到预测模型中,减轻不平衡数据集对模型性能的影响。【结果】在收集于白求恩医院的小型心衰数据集上进行心衰患者30 d内死亡率预测实验。实验结果表明,MK-SVM-AdaBoost模型的准确率和召回率分别达到了85.63%和86.33%,优于现有方法,ROC曲线下与坐标轴围成的面积(area under curve,AUC)和其微观平均值(micro-mean AUC,MiA-AUC)分别达到了91.00%和92.00%,表明提出的模型具有良好的稳定性。【结论】提出的模型具有较高的准确率和稳定性,可以为医生的临床决策提供一定的参考。今后课题将继续对数据集进行扩充,并对分级预警进行研究,以便对患者进行更有效的评估。 | 刘晓玉 李灯熬 赵菊敏 | 2023 | 太原理工大学学报2023，54，5： | 0 |
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