半监督分类学习问题在生物信息学中的研究进展——以间谍算法为例
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摘要
近年来,随着生命科学研究的不断发展,生物信息学这个利用智能算法处理生物数据的新型交叉学科越来越受到科研工作者的关注.机器学习在智能算法的研究中占据极其重要的地位,而机器学习中的半监督分类学习在生物信息学中有着广泛应用.以半监督分类学习中的间谍算法为例,首先回顾了半监督分类学习的发展历程,分析了该方法的研究现状,然后描述了间谍算法在生物信息学研究中的应用,最后总结了间谍算法的优势和局限性,并且讨论了可以改进的方向和未来的发展.
In recent years,with the continuous development of life science research,bioinformatics,which uses intelligent algorithms to deal with biological data,is becoming more and more concerned by scientists from various fields.Machine learning occupies an extremely important position in the study of intelligent algorithms,and semi-supervised classification learning in machine learning has been widely used in bioinformatics.In this paper,the spy algorithm in semi-supervised classification learning is taken as an example.Firstly,the history of semi-supervised classification learning is reviewed,and the research status of the method is analyzed.Then the application of spy algorithm in bioinformatics research is described.Finally,the advantages and limitations of the spy algorithm are summarized,and the direction of improvement and the future development are discussed.
In recent years,with the continuous development of life science research,bioinformatics,which uses intelligent algorithms to deal with biological data,is becoming more and more concerned by scientists from various fields.Machine learning occupies an extremely important position in the study of intelligent algorithms,and semi-supervised classification learning in machine learning has been widely used in bioinformatics.In this paper,the spy algorithm in semi-supervised classification learning is taken as an example.Firstly,the history of semi-supervised classification learning is reviewed,and the research status of the method is analyzed.Then the application of spy algorithm in bioinformatics research is described.Finally,the advantages and limitations of the spy algorithm are summarized,and the direction of improvement and the future development are discussed.
引文
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[2] Gibas C,Jambeck P.Developing bioinformatics computer skills-an introduction to software tools for biological applications[M].DBLP,2001.
[3] 赵磊,刘利军,黄青松.计算机算法在生物信息学中的应用[J].化学与生物工程,2009,26(9):79-81.
[4] 可婷.部分监督分类问题的研究[D].北京:中国农业大学,2014.
[5] 吕佳.基于局部学习的半监督分类问题研究[D].呼和浩特:内蒙古大学,2012.
[6] Liu B,Lee WS,Yu PS,et al.Partially Supervised Classification of Text Documents[M].in Nineteenth International Conference on Machine Learning,2002.
[7] 甘海涛.半监督聚类与分类算法研究[D].武汉:华中科技大学,2014.
[8] Li G.Machine learning in fuel consumption prediction of aircraft[M].in IEEE International Conference on Cognitive Informatics,2010.
[9] Armes T,Refern M.Using Big Data and predictive machine learning in aerospace test environments[M].in Autotestcon,2013.
[10] Duda R O,Hart P E,Stork D G.Pattern classification[M].Wiley,2001.
[11] 孙即祥.现代模式识别[M].北京:高等教育出版社,2008.
[12] Michalski S R,Carbonell G J,Mitchell M T.Machine learning an artificial intelligence approach volume II[M].Springer,1984.
[13] Sommer R,Paxson V.Outside the Closed World:On Using Machine Learning for Network Intrusion Detection[M].in IEEE Symposium on Security and Privacy,2010.
[14] Winter P,Hermann E,Zeilinger M.Inductive Intrusion Detection in Flow-Based Network Data Using One-Class Support Vector Machines[M].in Ifip International Conference on New Technologies,Mobility and Security,2011.
[15] Shahshahani B M,Landgrebe D A.The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon[J].IEEE Transactions on Geoscience & Remote Sensing,1994,32(5):1087-1095.
[16] Lippmann R P.Pattern classification using neural networks[M].IEEE Press,1989.
[17] Miller D J,Uyar H S.A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data[J].Medical Imaging IEEE Transactions on,1997,9(5):571-577.
[18] 刘伟,谢红卫.基于生物信息学方法发现潜在药物靶标[J].生物化学与生物物理进展,2011,38(1):11-19.
[19] Shi J Y,Li J X,Lu H M.Predicting existing targets for new drugs base on strategies for missing interactions[J].Bmc Bioinformatics,2016,17(Suppl 8):282.