JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (3): 85-92, 101.doi: 10.6040/j.issn.1671-9352.0.2018.261

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Identification of large intergenic non-coding RNAs using random forest

Wei-na XU*(),Guang-le ZHANG,Shi-hong LI,Yuan-yuan CHEN,Qiang LI,Tao YANG,Ming-min XU,Ning QIAO,Liang-yun ZHANG()   

  1. College of Science, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
  • Received:2018-05-15 Online:2019-03-01 Published:2019-03-19
  • Contact: Wei-na XU E-mail:2015111001@njau.edu.cn;zlyun@njau.edu.cn
  • Supported by:
    国家自然科学基金资助项目(11571173);国家自然科学基金资助项目(11401311);国家自然科学基金资助项目(11601231)

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Abstract:

A data source for understanding lincRNAs′ regulatory mechanisms by accurate identification is provided. With the features of minimum free energy and signal-noise ratio, we remove the redundant features by feature contribution. Thus, we develop a machine learning model (random forest) based on random forest algorithm to identify lincRNAs. After inspecting with the same experimental dataset, we prove that the sensitivity, specificity and accuracy of this new method have reached 94.1%, 93.2% and 93.7%, which are higher than the current identification index of the methods of PhyloCSF, LncRNA-ID and CPC. The method proposed in this paper shows better robustness and effective classification.

Key words: long intergenic non-coding RNA, random forests algorithm, minimum free energy, signal-noise ratio

CLC Number: 

  • Q61

Fig.1

Power spectrum curves of lincRNA(A) and mRNA(B)"

Fig.2

The flow diagram of lincRNAs identification"

Fig.3

The distribution of lincRNA (A) and mRNA (B) sequences based on the MFE threshold is -500"

Fig.4

The distribution of lincRNA (A) and mRNA (B) sequences based on the SNR threshold is 3"

Fig.5

Effect of the number of features on the classification accuracy rate of RF model"

Table 1

The classification performance of ELM, SVM and RF based on the feature sets of F1 or not"

Algorithms Sn/% Sp/% ACC/% MCC
ELM 67.3 91.7 79.5 0.608
SVM 89.2 87.1 88.2 0.764
RF 94.1 93.2 93.7 0.873
ELM-F1 65.8 86.1 75.7 0.526
SVM-F1 85.7 84.3 84.8 0.696
RF-F1 86.3 88.0 87.6 0.685

Table 2

The classification performance of different sampling"

Sampling Sn/% Sp/% ACC/% MCC
Under-sampling 94.10 93.20 93.70 0.873
Unsettled 86.18 96.49 93.46 0.832
Over-sampling 94.43 91.32 92.88 0.858

Table 3

The classification performance of different dataset"

Dataset Sn/% Sp/% ACC/% MCC
Balanced training dataset 94.1 93.2 93.7 0.873
Unbalanced training dataset 93.2 91.3 91.7 0.787

Fig.6

Receiver operating characteristic (ROC) curves of three different tools on the same dataset"

Table 4

Performance comparison of our prediction model with other methods for identifying known lincRNAs"

Algorithms Sn/％ Sp/％ ACC/％ MCC
PhyloCSF 84.7 73.2 78.7 0.578
LncRNA-ID 85.8 83.9 84.8 0.698
CPC 86.4 88.1 87.7 0.687
RF 94.1 93.2 93.7 0.873
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