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Related Concept Videos

Salivary Glands and Saliva01:23

Salivary Glands and Saliva

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The salivary glands, of which there are three pairs known as the parotid, submandibular, and sublingual glands, play a crucial role in maintaining oral health and initiating the digestive process. Positioned near the ears, beneath the masseter muscle, the parotid glands secrete saliva into the oral cavity through the parotid duct of Stensen. Meanwhile, the submandibular glands, located on the floor of the mouth, secrete saliva through channels named submandibular ducts. The sublingual glands,...
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Sampling Human Indigenous Saliva Peptidome Using a Lollipop-Like Ultrafiltration Probe: Simplify and Enhance Peptide Detection for Clinical Mass Spectrometry
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CapsNet-SSP: multilane capsule network for predicting human saliva-secretory proteins.

Wei Du1, Yu Sun1, Gaoyang Li1

  • 1Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.

BMC Bioinformatics
|June 11, 2020
PubMed
Summary

This study introduces CapsNet-SSP, a deep learning model for identifying saliva-secretory proteins from sequence data. This novel approach enhances the discovery of salivary protein biomarkers for clinical applications.

Keywords:
Capsule networkConvolutional neural networkDeep learningSaliva-secretory protein

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Saliva biomarkers offer noninvasive advantages over blood or urine for clinical diagnostics.
  • Identifying human saliva-secretory proteins is crucial for developing new clinical tests.
  • Existing methods for predicting saliva-secretory proteins often rely on feature engineering and conventional machine learning.

Purpose of the Study:

  • To develop an advanced deep learning model for predicting saliva-secretory proteins using only sequence information.
  • To improve the accuracy and efficiency of identifying potential salivary protein biomarkers.
  • To provide a computational tool for researchers investigating salivary gland-related diseases.

Main Methods:

  • An end-to-end deep learning model, multilane capsule network (CapsNet), was developed for saliva-secretory protein identification.
  • The model, CapsNet-SSP, utilizes differently sized convolution kernels to process protein sequence data.
  • Performance was evaluated against conventional machine learning algorithms and other deep learning architectures.

Main Results:

  • The CapsNet-SSP model demonstrated superior performance compared to existing conventional machine learning methods.
  • It also outperformed other state-of-the-art deep learning models commonly used for biological sequence analysis.
  • Predicted saliva-secretory proteins showed statistical significance when compared with known salivary cancer biomarkers.

Conclusions:

  • A novel CapsNet-based model (CapsNet-SSP) effectively identifies saliva-secretory proteins from sequence data.
  • The model offers improved performance over existing prediction methods.
  • A web server for CapsNet-SSP is available, aiding researchers in identifying salivary protein biomarkers for disease research.