CHEN Xiao-feng,WANG Shi-tong,CAO Su-qun.Gene function analysis of semisupervised multilabel learning[J].CAAI Transactions on Intelligent Systems,2008,3(01):83-90.





Gene function analysis of semisupervised multilabel learning
CHEN Xiao-feng1 WANG Shi-tong1 CAO Su-qun12
1.School of Information Technology, Jiangnan University, Wuxi 214122 , Ch ina;
 2.Department of Mechanical Engineering, Huaiyin Institute of Technology, H uai’an 223001,China
semisupervised multilabel selftraining support vector machine
传统的机器学习主要解决单标记学习,即一个样本仅有一个标记.在生物信息学中,一个基因通常至少具有一个功能,即至少具有一个标记,与传统学习方法相比,多标记学习能更有效地识别生物相关基因组的功能.目前的研究主要集中在监督多标记学习算法.然而,研究半监督多标记学习算法,从已标记和未标记的基因表达数据中学习,仍然是未解决问题.提出一种有效的基因功能分析的半监督多标记学习算法SML_SVM.首先,SML_SVM根据PT 4方法,将半监督多标记学习问题转化为半监督单标记学习问题,然后根据最大后验概率原则(MAP)和K近邻方法估计未标记样本的标记,最后,用SVM求解单标记学习问题.在yeast 基因数据和genbase蛋白质数据上的实验表明,SML_SVM性能比基于PT4方法的MLSVM和自训练 MLSVM更优
Conventional machine learning is used only for single label learning, implying that every sample has only one label. However, in bioinformatics, a gen e has more than one function, so it needs more than one label. Therefore, multi  label learning is more effective for identifying gene groups than conventional l earning approach. Current research mainly focuses on supervised multilabel lea r ning. The problem of effective semisupervised multilabel learning strategies f or labeled examples and unlabeled examples of gene expression datasets still rem ains unsolved. In this paper, a semisupervised multilabel learning algorithm , named SML_SVM, is presented as an effective multilabel learner for analysis of gene expressions with at least one function. First, the proposed SML_SVM algorit hm transforms the semisupervised multilabel learning into corresp ond ing semisupervised singlelabel learning by the PT4 method, then it labels un la beled examples using the maximum a posteriori (MAP) principle in combination wit h the Knearest neighbor method, and finally, it solves the corresponding singl e label learning problem using SVM. The distinctive characteristic of the propos e d algorithm is its efficient integration of SVMbased singlelabel learning wi th MAP and Knearest neighbor methods. Experimental results with a real Yeast gen e expression dataset and a Genbase protein dataset show that the proposed SML_S VM algorithm outperforms the PT4based MLSVM method and selftraining MLSVM.


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国家自然科学基金资助项目(6077320 6/F020106,60704047/F030304);国防应用基础研究基金资助项目(A1420461266);
更新日期/Last Update: 2009-05-10