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In Silico Identification of Anticancer Peptides with Stacking Heterogeneous Ensemble Learning Model and Sequence Information

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Intelligent Computing Theories and Application (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11644))

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Abstract

Cancer is a well-known dreadful killer of human being’s health, which has led to countless deaths and misery. Traditional treatment can also affect the normal cells while killing cancer cells. Meanwhile, physical or chemical techniques are costly and inefficient. Fortunately, anticancer peptides are a promising treatment, with specifically targeted, low production cost and other advantages. In order to effectively identify the anticancer peptides, we proposed a stacking heterogeneous ensemble learning model, ACP-SE, for predicting anticancer peptides. More specifically, to fully exploit protein sequence information, we developed an efficient feature representation approach by integrating binary profile feature and conjoint triad feature. Then we use a stacking ensemble strategy to combine the three heterogeneous classifiers and get the final prediction results. It was demonstrated that the proposed ACP-SE remarkably outperformed other comparison methods.

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Acknowledgments

This work is supported by the National Science Foundation of China, under Grants 61572506, in part by the NSFC Excellent Young Scholars Program, under Grants 61722212, in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences.

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Authors and Affiliations

  1. The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China

    Hai-Cheng Yi, Zhu-Hong You, Yan-Bin Wang, Zhan-Heng Chen & Zhen-Hao Guo

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Hai-Cheng Yi, Zhu-Hong You, Zhan-Heng Chen & Zhen-Hao Guo

  3. School of Computer Science and Communication Engineering, Jiangsu University, Zhen Jiang, 212013, China

    Hui-Juan Zhu

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  1. Hai-Cheng Yi
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  2. Zhu-Hong You
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  3. Yan-Bin Wang
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  4. Zhan-Heng Chen
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  5. Zhen-Hao Guo
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  6. Hui-Juan Zhu
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Contributions

Hai-Cheng Yi and Zhu-Hong You conceived the algorithm, carried out analyses, prepared the data sets, carried out experiments, and wrote the manuscript; Yan-Bin Wang, Zhan-Heng Chen, Zhen-Hao Guo and Hui-Juan Zhu designed, performed and analyzed experiments and wrote the manuscript; All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zhu-Hong You .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

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Yi, HC., You, ZH., Wang, YB., Chen, ZH., Guo, ZH., Zhu, HJ. (2019). In Silico Identification of Anticancer Peptides with Stacking Heterogeneous Ensemble Learning Model and Sequence Information. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_30

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