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Identifying lncRNA Based on Support Vector Machine

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Health Information Science (HIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11837))

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Abstract

With the development of high-throughput sequencing technology, it brings a large volume of data of transcriptome. Long non-protein-coding RNAs (lncRNAs) identification is pervasive in transcriptome studies in their important roles in biological process. This paper proposed a computational method for identifying lncRNAs based on machine learning. The method first selects feature using k-mer for traversing the transcript sequence to obtain a large class of features, integrated GC content and sequence length. Then it uses variance test to select three kinds of features by grid searching and reduce the data dimension and support vector machine pressure to establish a recognition model, the final model has a certain stability and robustness. The method obtain 95.7% accuracy, 0.99 AUC for test dataset. Therefore, it could be promising for identifying lncRNA.

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References

  1. Bu, D., et al.: NONCODE v3.0: integrative annotation of long noncoding RNAs. Nucleic Acids Res 40, D210–D215 (2012)

    Article  Google Scholar 

  2. Derrien, T., et al.: The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 22, 1775–1789 (2012)

    Article  Google Scholar 

  3. Cheetham, S.W., Gruhl, F., Mattick, J.S., Dinger, M.E.: Long nonconding RNAs and the genetics of cancer. Br. J. Cancer 108, 2419–2425 (2013)

    Article  Google Scholar 

  4. Li, D., Chen, G., Yang, J., Fan, X., Gong, Y., Xu, G., et al.: Transcriptome analysis reveals disti-nct patterns of long nonconding RNAs in heart and plasma of mice with heart failure. PLoS ONE 8, e77938 (2013)

    Article  Google Scholar 

  5. Chen, L., Guo, X., Li, Z., He, Y.: Relationship between long non-coding RNAs and Alzheimer’s disease: a systematic review. Pathol. Res. Pract. 215(1), 12–20 (2019)

    Article  Google Scholar 

  6. Li, D., Chen, G., Yang, J., et al.: Transcriptome analysis reveals distinct patterns of long noncoding RNAs in heart and plasma of mice with heart failure. PLoS ONE 8(10), e77938 (2013)

    Article  Google Scholar 

  7. Vucicevic, D., Schrewe, H., Orom, U.A., et al.: Molecular mechanisms of long ncRNAs in neurological disorders. Front. Genet. 4, 48 (2014)

    Google Scholar 

  8. Gutschner, T., Hammerle, M., Eissmann, M., et al.: The noncoding RNA MALAT1 is a critical regulator of the metastasis phenotype of lung cancer cells. Cancer Res. 73(3), 1180–1189 (2013)

    Article  Google Scholar 

  9. Bao, Z., Yang, Z., Huang, Z., et al.: LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases. Nucleic Acids Res. 47(D1), D1034 (2019)

    Article  Google Scholar 

  10. Wang, H., Hu, H., Xiang, Z., Lu, C., Dai, F., Tong, X.: Identification and characterization of a new long noncoding RNA iab-1 in the Hox cluster of silkworm Bombyx mori identification of iab-1. J. Cell Biochem. 120(10), 17283–17292 (2019)

    Google Scholar 

  11. Zhang, Y., Wang, X., Kang, L.: A k-mer scheme to predict piRNAs and characterize locust piRNAs. Bioinformatics 27(6), 771–776 (2011)

    Article  Google Scholar 

  12. Banerjee, T., Gupta, S., Ghosh, T.C.: Role of mutational bias and natural selection on genome-wide nucleotide bias in prokaryotic organisms. Biosystems 81(1), 11–18 (2005)

    Article  Google Scholar 

  13. Kong, L., Zhang, Y., Ye, Z.Q.: CPC: Assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res. 35, W345–W349 (2007)

    Article  Google Scholar 

  14. Zhou, Z.H.: Machine Learning. Tsinghua University Press, Beijing (2016)

    Google Scholar 

  15. Pang, H.-X., Dong, W.-X.: Novel linear search for support vector machine parameter selection. J. Zhejiang Univ. Sci. C 12, 885 (2011)

    Article  Google Scholar 

  16. Derrien, T.: The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 22, 1775–1789 (2013)

    Article  Google Scholar 

  17. Achawanantakun, R., Chen, J., Sun, Y., Zhang, Y.: LncRNA-ID: long non-coding RNA IDentification using balanced random forests. Bioinformatics 31(24), 3897–3905 (2015)

    Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (61502243, 61502247, 61572263), China Postdoctoral Science Foundation (2018M632349), Zhejiang Engineering Research Center of Intelligent Medicine under 2016E10011, Natural Science Foundation of the Higher Education Institutions of Jiangsu Province in China (No. 16KJD520003).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Yongmin Li, Yang Ou & Lejun Gong

  2. Nanjing Foreign Language School, Nanjing, 210008, China

    Zhe Xu

Authors
  1. Yongmin Li
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  2. Yang Ou
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  3. Zhe Xu
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  4. Lejun Gong
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Corresponding author

Correspondence to Lejun Gong .

Editor information

Editors and Affiliations

  1. Victoria University, Footscray, VIC, Australia

    Hua Wang

  2. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  4. Instituto de Salud Carlos III, Madrid, Spain

    Fernando Martin-Sanchez

  5. Victoria University, Footscray, VIC, Australia

    Yanchun Zhang

  6. Vrije University, Amsterdam, The Netherlands

    Zhisheng Huang

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Li, Y., Ou, Y., Xu, Z., Gong, L. (2019). Identifying lncRNA Based on Support Vector Machine. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-32962-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32961-7

  • Online ISBN: 978-3-030-32962-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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