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Identification of MicroRNA Precursors with Support Vector Machine and string kernel

Genomics, Proteomics & Bioinformatics 2008年第2期6卷 121-128页

作者： Jian-Hua Xu Fei Li Qiu-Feng Sun Department of Computer Science Nanjing Normal University Nanjing 210097 China Department ofEntomology Nanjing Agricultural University Nanjing 210095 China

MicroRNAs （miRNAs） are one family of short （21-23 nt） regulatory non-coding RNAs processed from long （70-110 nt） miRNA precursors （pre-miRNAs）. Identifying true and false precursors plays an important role in computational identification of miRNAs. Some numerical features have been extracted from precursor sequences and their secondary structures to suit some classification methods; however, they may lose some usefully discriminative information hidden in sequences and structures. In this study, pre-miRNA sequences and their secondary structures are directly used to construct an exponential kernel based on weighted Levenshtein distance between two sequences. This string kernel is then combined with support vector machine （SVM） for detecting true and false pre-miRNAs. Based on 331 training samples of true and false human pre-miRNAs, 2 key parameters in SVM are selected by 5-fold cross validation and grid search, and 5 realizations with different 5-fold partitions are executed. Among 16 independent test sets from 3 human, 8 animal, 2 plant, 1 virus, and 2 artificially false human pre-miRNAs, our method statistically outperforms the previous SVM-based technique on 11 sets, including 3 human, 7 animal, and 1 false human pre-miRNAs. In particular, premiRNAs with multiple loops that were usually excluded in the previous work are correctly identified in this study with an accuracy of 92.66%.

关键词： string kernel support vector machine microRNA precursor weighted Levenshteindistance

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维普期刊数据库

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In silico toxicity prediction by support vector machine and SMILES representation-based string kernel

In silico toxicity prediction by support vector machine and ...

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第十一届全国计算（机）化学学术会议

作者：曹东升许青松梁逸曾 Research center of modernization of traditional Chinese medicines Central South UniversityChangsha 410083P.R.China School of Mathematical Sciences and Computing Technology Central South UniversityChangsha 410083P.R.China

＜正＞There is a great need to assess the harmful effects or toxicities of chemicals to which man is *** the present paper,the simplified molecular input line entry specification（SMILES） representation-based string kernel,together with the state-of-the-art support vector machine（SVM） algorithm,were developed to classify the toxicity of chemicals from Distributed Structure-Searchable Toxicity（DSSTox） database *** this method,the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds（double,triple,and stereochemistry）.Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the *** model validation approaches,five-fold cross-validation and independent validation set,were used for assessing the predictive capability of our developed models（see Figure 1）.The results obtained indicate that SVM based on SMILES string kernel can be regarded as a very promising and alternative modeling approach for potential toxicity prediction of *** datasets can be downloaded from the website: http://***/p/data-share/.

关键词： toxicity prediction structure-toxicity relationship(STR) support vector machine(SVM) simplified molecular input line entry specification(SMILES) string kernel

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On Using Physico-Chemical Properties of Amino Acids in string kernels for Protein Classification via Support Vector Machines

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Journal of Systems Science & Complexity 2015年第2期28卷 504-516页

作者： LI Limin AOKI-KINOSHITA Kiyoko F CHING Wai-Ki JIANG Hao Institute of Information and System Science Xi'an Jiaotong University Department of Bioinformatics Faculty of EngineeringSoka University Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong Department of Mathematics School of InformationRenmin University of China

string kernels are popular tools for analyzing protein sequence data and they have been successfully applied to many computational biology problems. The traditional string kernels assume that different substrings are independent. However, substrings can be highly correlated due to their substructure relationship or common physico-chemical properties. This paper proposes two kinds of weighted spectrum kernels： The correlation spectrum kernel and the AA spectrum kernel. We evMuate their performances by predicting glycan-binding proteins of 12 glycans. The results show that the correlation spectrum kernel and the AA spectrum kernel perform significantly better than the spectrum kernel for nearly all the 12 glycans. By comparing the predictive power of AA spectrum kernels constructed by different physico-chemical properties, the authors can also identify the physico- chemical properties which contributes the most to the glycan-protein binding. The results indicate that physico-chemical properties of amino acids in proteins play an important role in the mechanism of glycamprotein binding.

关键词： AAindex AA spectrum kernel correlation spectrum kernel physico-chemical properties string kernel weighted spectrum kernel.

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维普期刊数据库

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kernel k-nearest neighbor algorithm as a flexible SAR modeling tool

Kernel k-nearest neighbor algorithm as a flexible SAR modeli...

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中国化学会第28届学术年会

作者： Dong-Sheng Cao1 Jian-Hua Huang1 Jun Yan1 Qing-Song Xu2 Yi-Zeng Liang*1 1Research center of modernization of traditional Chinese medicines Central South University Changsha 410083 P.R.China 2School of Mathematical Sciences and Computing Technology Central South University Changsha 410083 P.R.China Research center of modernization of traditional Chinese medicines Central South UniversityChangsha 410083 P.R. China School of Mathematical Sciences and Computing Technology Central South UniversityChangsha 410083 P.R. China

A kernel version of k-nearest neighbor algorithm （k-NN） has been developed to model the complex relationship between molecular descriptors and bioactivities of *** k-NN is to perform the original k-NN algorithm by mapping the training samples in the input space into a high-dimensional feature *** can be easily constructed by calculating the distance between samples in the feature space,directly deriving from the simple calculation of the kernel *** developed kernel k-NN is very flexible to deal with complex nonlinear relationship,more importantly;it can also conveniently cope with some non-vectorial data only by the definition of different *** results obtained from several real SAR datasets indicated that the performance of kernel k-NN is comparable to support vector machine *** can be regarded as an alternative modeling technique for several chemical problems including the study of structure-activity relationship （SAR）.The source codes implementing kernel k-NN in R language are freely available at http://***/p/kernelmethods/.

关键词： k-nearest neighbor (k-NN) kernel methods string kernel structure-activity relationship (SAR).

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