Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (2): 76-82.doi: 10.6040/j.issn.1672-3961.0.2019.292

• Machine Learning & Data Mining • Previous Articles     Next Articles

Prediction of microRNA-binding residues based on Laplacian support vector machine and sequence information

Xin MA1(),Xue WANG2   

  1. 1. School of Statistics and Mathematics, Nanjing Audit University, Nanjing 211815, Jiangsu, China
    2. Experimental Center, Nanjing Audit University, Nanjing 211815, Jiangsu, China
  • Received:2019-06-06 Online:2020-04-20 Published:2020-04-16

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Abstract:

A new method of semi-surpervised learning algorithm was proposed to predict miRNA-binding residues in protein sequences. The Laplacian support vector machine (LapSVM) algorithm was combined with the newly proposed hybrid features to build a prediction model. The hybrid features were obtained from a combination of secondary structure information, HKM features, and the newly proposed feature combination of amino acid physicochemical properties and evolutionary information. Performance comparison of the various features indicated that our novel feature contributed the most to prediction improvement. The results demonstrated that accuracy of our LapSVM model achieved 88.72%, sensitivity achieved 54.18% and specificity achieved 91.15% using feature selection. The LapSVM model significantly outperformed other approaches at miRNA-binding site prediction.

Key words: microRNA-binding residues, Laplacian support vector machine, evolutionary information, physicochemical properties, feature selection

CLC Number: 

  • Q811.4

Table 1

Main dataset collected from PDB and UniProt"

数据库 蛋白质序列ID号
PDB 2LI8_A，2N82_B，3A6P_A，3A6P_C，3ADI_A，3ADL_A，3TRZ_A，4L8R_C，4NGB_A，4QOZ_B，4W5N_A
UniProt O04379，O04492，P09651，P43243，P48432，P98175，Q01860，Q06787，Q1PRL4，Q2VB19，Q3UHX9，Q4R979，Q5D1E8，Q5RCW2，Q6GLC9，Q80U58，Q8CJF8，Q8K3Y3，Q8R205，Q8R418，Q9JIK5，Q9R0B7，Q9SKN5，Q9U489，Q9UGR2，Q9XGW1，Q9ZVD5

Table 2

Predictive performance of the LapSVM model based on various features"

特征 准确率 敏感性 特异性 MCC
PSSM 0.711 2 0.215 7 0.748 2 0.021
PSSMPP 0.782 0 0.333 3 0.815 5 0.096
PSSMPP+SS 0.836 5 0.451 0 0.865 3 0.221
PSSMPP+HKM 0.831 6 0.414 2 0.861 1 0.188
PSSMPP+SS+HKM 0.875 2 0.493 3 0.903 5 0.291
Optimal 44 features 0.887 2 0.541 8 0.911 5 0.334

Fig.1

MCC values curve of the model constructed from 165 feature subsets"

Table 3

Performance comparison with other machine learning algorithms"

特征 准确率 敏感性 特异性 MCC
RF 0.739 8 0.352 9 0.768 7 0.072
SVM 0.410 1 0.137 3 0.430 5 0.000
LapSVM 0.887 2 0.541 8 0.911 5 0.334
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