多項式馬可夫簡易貝氏分類器結合狄氏先驗分配於基因序列分類之研究

翁慈宗; 劉冠良; 韓昀達

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P87 - 115

基础与应用科学 > 资讯科学
社会科学 > 管理学

DOI： 10.6382/JIM

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多项式马可夫简易贝氏分类器结合狄氏先验分配於基因序列分类之研究

Dirichlet Priors for Markov Naïve Bayesian Classifiers with Multinomial Model for Gene Sequence Data

翁慈宗(Tzu-Tsung Wong) ；刘冠良(Kuan-Liang Liu) ；韩昀达(Yun-Da Han)

资讯管理学报； 22 卷 1 期 (2015 / 01 / 01) ， P87 - 115

繁体中文

狄氏分配；马可夫模型；简易贝氏分类器；核甘酸基因序列； Dirichlet distribution ； Markov model ； naïve Bayesian classifier ； rRNA gene sequence

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摘要 │ 参考文献 (24) │ 国际计量

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Purpose-The RDP classifier is computationally efficient and does not require sequence alignment. It also works well with short sequences and provides a unique niche for applications using the NGS technologies that generate millions of short sequences. The performance however, is hampered by the conditional independent assumption on features. The dependency is especially obvious in attributes by which the k-mer method extracts sequences from sequences with sliding window where each attribute is overlapped by k-1 base with its previous and next attribute. Design/methodology/approach-This study developed a multinomial Markovbased Bayesian classifier which remedies the unrealistic independent assumption by Markov model. In order to prevent probability estimate of feature to become zero and distort the classification result, Laplace estimate is usually utilized as a prior for all features. However, the setting assumes a fix confidence level for all features. In this study, we further develop a noninformative generalized distribution for prior setting that will allow different confidence level settings for different features. Findings-The experimental results on bacterial 16S and fungal 28S rRNA gene sequence sets show that the proposed model can achieve higher prediction accuracy than the well-known RDP classifier in all ranks. Since the number of priors for a class value in the Markov naïve Bayesian classifier is two instead of one in the naïve Bayesian classifier, the best noninformative Dirichlet priors do enhance the performance of the Markov naïve Bayesian classifier. Research limitations/implications - The study proposes to model DNA sequences as a k^(th) order Markov chain on the alphabet A,C,G and T. That is, the probability of observing a particular symbol only depends on the k previous one. Since under this model, the probability of a read can be written as a ratio of products of the probability of overlapping k-mers, it does introduce additional computational overheads to the current implementation of the RDP classifier. However, the overhead is just one time calculation during training process practically. Practical implications-Since the ability to obtain thousands of rRNA sequences from environmental and Human Microbiome Project samples using high-throughput sequencing technologies has become a reality, accurate sequence classification is a critical component of ecological interpretation of environmental datasets. The approach used in this article to evaluate bacterial and fungal sequences proves to be a valuable tool to determine the most important factors affecting classification accuracy. Originality/value - This study develops a Markov Bayesian classifier that remedies the strong conditional assumption on naïve Bayesian classifier by Markov assumption. Since the k-mer method uses an overlapping sliding window to extract features from raw sequence data, the conditional independent assumption on features is clearly violated. In the opposite, the Markov model that assumes each feature is dependent on previous features fits k-mer extraction method perfectly.

参考文献 ( 24 ) 〈TOP〉

Baum, L.E.,Eagon, J.A.(1967).An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology.Bulletinof theAmerican Mathematical Society,73(3),360-363.
Brady, A.,Salzberg, S.L.(2009).Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models.Nature Methods,6(9),673-678.
Camacho, C.,Coulouris, G.,Avagyan, V.,Ma, N.,Papadopoulos, J.,Bealer, K.,Madden, T.L.(2009).BLAST+: architecture and applications.BMC Bioinformatics,10(1),421.
Chen, J.,Huang, H.,Tian, S.,Qu, Y.(2009).Feature selection for text classification with Naïve Bayes.Expert Systems with Applications,36(3),5432-5435.
Cole, J.R.,Wang, Q.,Fish, J.A.,Chai, B.,McGarrell, D.M.,Sun, Y.,Brown, C.T.,Porras-Alfaro, A.,Kuske, C.R.,Tiedje, J.M.(2014).Ribosomal database project: Data and tools for high throughput rRNA analysis.Nucleic Acids Research,42(1),633-642.

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