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. 2024 Feb 7:14:1277121.
doi: 10.3389/fmicb.2023.1277121. eCollection 2023.

Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model

Wenzheng Bao #  1 , Yujun Liu #  1 , Baitong Chen  2   3
Affiliations

Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model

Wenzheng Bao et al. Front Microbiol. .

Abstract

Introduction: The oral microbial group typically represents the human body's highly complex microbial group ecosystem. Oral microorganisms take part in human diseases, including Oral cavity inflammation, mucosal disease, periodontal disease, tooth decay, and oral cancer. On the other hand, oral microbes can also cause endocrine disorders, digestive function, and nerve function disorders, such as diabetes, digestive system diseases, and Alzheimer's disease. It was noted that the proteins of oral microbes play significant roles in these serious diseases. Having a good knowledge of oral microbes can be helpful in analyzing the procession of related diseases. Moreover, the high-dimensional features and imbalanced data lead to the complexity of oral microbial issues, which can hardly be solved with traditional experimental methods.

Methods: To deal with these challenges, we proposed a novel method, which is oral_voting_transfer, to deal with such classification issues in the field of oral microorganisms. Such a method employed three features to classify the five oral microorganisms, including Streptococcus mutans, Staphylococcus aureus, abiotrophy adjacent, bifidobacterial, and Capnocytophaga. Firstly, we utilized the highly effective model, which successfully classifies the organelle's proteins and transfers to deal with the oral microorganisms. And then, some classification methods can be treated as the local classifiers in this work. Finally, the results are voting from the transfer classifiers and the voting ones.

Results and discussion: The proposed method achieved the well performances in the five oral microorganisms. The oral_voting_transfer is a standalone tool, and all its source codes are publicly available at https://github.com/baowz12345/voting_transfer.

Keywords: bioinformatics; classification; machine learning; oral microorganisms proteins; voting transfer model.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The flowchart of Oral_voting_transfer model
FIGURE 2
FIGURE 2
The ROC of Streptococcus mutans active sites. (A) Is the ROC curve of 10-fold cross validation with TAGPPI. (B) Is the ROC curve of 10-fold cross validation with SeqVec. (C) Is the ROC curve of 10-fold cross validation with ProSE.
FIGURE 3
FIGURE 3
The ROC of Streptococcus mutans binding sites. (A) Is the ROC curve of 10-fold cross validation with TAGPPI. (B) Is the ROC curve of 10-fold cross validation with SeqVec. (C) Is the ROC curve of 10-fold cross validation with ProSE.
FIGURE 4
FIGURE 4
The ROC of Staphylococcus aureus active sites. (A) Is the ROC curve of 10-fold cross validation with TAGPPI. (B) Is the ROC curve of 10-fold cross validation with SeqVec. (C) Is the ROC curve of 10-fold cross validation with ProSE.
FIGURE 5
FIGURE 5
The ROC of Staphylococcus aureus binding sites. (A) Is the ROC curve of 10-fold cross validation with TAGPPI. (B) Is the ROC curve of 10-fold cross validation with SeqVec. (C) Is the ROC curve of 10-fold cross validation with ProSE.
FIGURE 6
FIGURE 6
The ROC of Abiotrophia adjacens active sites. (A) Is the ROC curve of 10-fold cross validation with TAGPPI. (B) Is the ROC curve of 10-fold cross validation with SeqVec. (C) Is the ROC curve of 10-fold cross validation with ProSE.
FIGURE 7
FIGURE 7
The ROC of Abiotrophia adiacens binding sites. (A) Is the ROC curve of 10-fold cross validation with TAGPPI. (B) Is the ROC curve of 10-fold cross validation with SeqVec. (C) Is the ROC curve of 10-fold cross validation with ProSE.
FIGURE 8
FIGURE 8
The ROC of Bifidobacterial active sites. (A) Is the ROC curve of 10-fold cross validation with TAGPPI. (B) Is the ROC curve of 10-fold cross validation with SeqVec. (C) Is the ROC curve of 10-fold cross validation with ProSE.
FIGURE 9
FIGURE 9
The ROC of Bifidobacterial binding sites. (A) Is the ROC curve of 10-fold cross validation with TAGPPI. (B) Is the ROC curve of 10-fold cross validation with SeqVec. (C) Is the ROC curve of 10-fold cross validation with ProSE.
FIGURE 10
FIGURE 10
The ROC of Capnocytophaga active sites. (A) Is the ROC curve of 10-fold cross validation with TAGPPI. (B) Is the ROC curve of 10-fold cross validation with SeqVec. (C) Is the ROC curve of 10-fold cross validation with ProSE.
FIGURE 11
FIGURE 11
The ROC of Capnocytophaga binding sites. (A) Is the ROC curve of 10-fold cross validation with TAGPPI. (B) Is the ROC curve of 10-fold cross validation with SeqVec. (C) Is the ROC curve of 10-fold cross validation with ProSE.
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