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Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks

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Abstract

The diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-cancer peptides, this paper proposes a more suitable feature grouping sequence and spatial dimension-integrated network algorithm for anti-cancer peptide sequence prediction called GRCI-Net. The main process is as follows: First, we implemented the fusion reduction of binary structure features and K-mer sparse matrix features through principal component analysis and generated a set of new features; second, we constructed a new bidirectional long- and short-term memory network. We used traditional convolution and dilated convolution to acquire features in the spatial dimension using the memory network's grouping sequence model, which is designed to better handle the diversification of anti-cancer peptide feature sequences and to fully learn the contextual information between features. Finally, we achieved the fusion of grouping sequence features and spatial dimensional integration features through two sets of dense network layers, achieved the prediction of anti-cancer peptides through the sigmoid function, and verified the approach with two public datasets, ACP740 (accuracy reached 0.8230) and ACP240 (accuracy reached 0.8750). The following is a link to the model code and datasets mentioned in this article: https://github.com/ YouHongfeng101/ACP-DL.

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Authors and Affiliations

  1. College of Information Science and Engineering, Xinjiang University, 666 Shengli Road, Tianshan District, Urumqi, Xinjiang, China

    Hongfeng You

  2. Network Center, Xinjiang University, Xinjiang, China

    Long Yu

  3. School of Software, Xinjiang University, Tianshan District, 666 Shengli Road, Urumqi, Xinjiang, China

    Shengwei Tian

  4. Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China

    Xiang Ma

  5. Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, No. 137, LiYuShan South Road, Urumqi, Xinjiang, China

    Yan Xing

  6. College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China

    Jinmiao Song

  7. People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China

    Weidong Wu

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  1. Hongfeng You
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  2. Long Yu
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Correspondence to Long Yu.

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You, H., Yu, L., Tian, S. et al. Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks. Interdiscip Sci Comput Life Sci 14, 196–208 (2022). https://doi.org/10.1007/s12539-021-00481-0

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  • DOI: https://doi.org/10.1007/s12539-021-00481-0

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