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RPML: A Learning-Based Approach for Reranking Protein-Spectrum Matches

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Intelligent Computing Theories and Application (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10954))

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Abstract

Searching top-down spectra against a protein database has been a mainstream method for intact protein identification. Ranking true Protein-Spectrum Matches (PrSMs) over their false counterparts is a feasible method for improving protein identification results. In this paper, we propose a novel model called RPML (Rerank PrSMs based on Machine Learning) to rerank PrSMs in top-down proteomics. The experimental results on real data sets show that RPML can distinguish more correct PrSMs from incorrect ones. The source codes of algorithm are available at https://github.com/dqiong/spectra_protein_match_rerank.

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Acknowledgements

This work was partially supported by the Natural Science Foundation of China (Nos. 61572094, 61502071), the Fundamental Research Funds for the Central Universities (Nos. DUT2017TB02, DUT14QY07) and the Science-Technology Foundation for Youth of Guizhou Province (No. KY[2017]250).

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Authors and Affiliations

  1. School of Software, Dalian University of Technology, Dalian, China

    Qiong Duan, Hao Liang, Chaohua Sheng, Bo Xu & Zengyou He

  2. School of Information Engineering, Zunyi Normal University, Zunyi, China

    Jun Wu

Authors
  1. Qiong Duan
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  2. Hao Liang
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  3. Chaohua Sheng
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  4. Jun Wu
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  5. Bo Xu
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  6. Zengyou He
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Corresponding authors

Correspondence to Bo Xu or Zengyou He .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Phalguni Gupta

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Duan, Q., Liang, H., Sheng, C., Wu, J., Xu, B., He, Z. (2018). RPML: A Learning-Based Approach for Reranking Protein-Spectrum Matches. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_54

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  • DOI: https://doi.org/10.1007/978-3-319-95930-6_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95929-0

  • Online ISBN: 978-3-319-95930-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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