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DenSec: Secreted Protein Prediction in Cerebrospinal Fluid Based on DenseNet and Transformer

Author

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  • Lan Huang

    (Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Yanli Qu

    (Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Kai He

    (Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Yan Wang

    (Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Dan Shao

    (College of Computer Science and Technology, Changchun University, Changchun 130022, China)

Abstract

Cerebrospinal fluid (CSF) exists in the surrounding spaces of mammalian central nervous systems (CNS); therefore, there are numerous potential protein biomarkers associated with CNS disease in CSF. Currently, approximately 4300 proteins have been identified in CSF by protein profiling. However, due to the diverse modifications, as well as the existing technical limits, large-scale protein identification in CSF is still considered a challenge. Inspired by computational methods, this paper proposes a deep learning framework, named DenSec, for secreted protein prediction in CSF. In the first phase of DenSec, all input proteins are encoded as a matrix with a fixed size of 1000 × 20 by calculating a position-specific score matrix (PSSM) of protein sequences. In the second phase, a dense convolutional network (DenseNet) is adopted to extract the feature from these PSSMs automatically. After that, Transformer with a fully connected dense layer acts as classifier to perform a binary classification in terms of secretion into CSF or not. According to the experiment results, DenSec achieves a mean accuracy of 86.00% in the test dataset and outperforms the state-of-the-art methods.

Suggested Citation

  • Lan Huang & Yanli Qu & Kai He & Yan Wang & Dan Shao, 2022. "DenSec: Secreted Protein Prediction in Cerebrospinal Fluid Based on DenseNet and Transformer," Mathematics, MDPI, vol. 10(14), pages 1-10, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2490-:d:865150
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    References listed on IDEAS

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    1. Debaditya Shome & T. Kar & Sachi Nandan Mohanty & Prayag Tiwari & Khan Muhammad & Abdullah AlTameem & Yazhou Zhang & Abdul Khader Jilani Saudagar, 2021. "COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare," IJERPH, MDPI, vol. 18(21), pages 1-14, October.
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