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Updated: Jun 14, 2025
Legionella pneumophila Outer Membrane Vesicles: Isolation and Analysis of Their Pro-inflammatory Potential on Macrophages
Published on: February 22, 2017
Deep Learning Prediction of Inflammatory Inducing Protein Coding mRNA in P. gingivalis Released Outer Membrane
Pradeep Kumar Yadalam1, Raghavendra Vamsi Anegundi1, Muthupandian Saravanan2
1Department of Periodontics, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India.
Aim:
The Insilco study uses deep learning algorithms to predict the protein-coding pg m RNA sequences.
Material And Methods:
The NCBI GEO DATA SET GSE218606's GEO R tool discovered P.G's outer membrane vesicles' most differentially expressed mRNA. Genemania analyzed differentially expressed gene networks. Transcriptomics data were collected and labeled on P. gingivalis protein-coding mRNA sequence and pseudogene, lincRNA, and bidirectional promoter lincRNA. Orange, a machine learning tool, analyzed and predicted data after preprocessing. Naïve Bayes, neural networks, and gradient descent partition data into training and testing sets, yielding accurate results. Cross-validation, model accuracy, and ROC curve were evaluated after model validation.
Results:
Three models, Neural Networks, Naive Bayes, and Gradient Boosting, were evaluated using metrics like Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Specificity. Gradient Boosting achieved a balanced performance (AUC: 0.72, CA: 0.41, F1: 0.32) compared to Neural Networks (AUC: 0.721, CA: 0.391, F1: 0.314) and Naive Bayes (AUC: 0.701, CA: 0.172, F1: 0.114). While statistical tests revealed no significant differences between the models, Gradient Boosting exhibited a more balanced precision-recall relationship.
Conclusion:
In silico analysis using machine learning techniques successfully predicted protein-coding mRNA sequences within Porphyromonas gingivalis OMVs. Gradient Boosting outperformed other models (Neural Networks, Naive Bayes) by achieving a balanced performance across metrics like AUC, classification accuracy, and precision-recall, suggests its potential as a reliable tool for protein-coding mRNA prediction in P. gingivalis OMVs.
Insights
This study used deep learning to predict protein-coding mRNA in Porphyromonas gingivalis outer membrane vesicles. Gradient Boosting showed the most balanced performance, indicating its potential for accurate mRNA sequence prediction.
Area of Science:
- Genomics and Bioinformatics
- Computational Biology
- Molecular Biology
Background:
- Porphyromonas gingivalis outer membrane vesicles (OMVs) are crucial in periodontitis pathogenesis.
- Identifying protein-coding mRNA sequences within OMVs is vital for understanding their function.
- Current methods for mRNA prediction in OMVs require enhancement.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting protein-coding mRNA sequences in P. gingivalis OMVs.
- To compare the performance of Naïve Bayes, Neural Networks, and Gradient Boosting algorithms for this task.
- To identify the most effective model for accurate and reliable mRNA sequence prediction.
Main Methods:
- Transcriptomic data from P. gingivalis OMVs were collected and annotated.
- Machine learning tools, including Orange, were used for data preprocessing and analysis.
- Naïve Bayes, Neural Networks, and Gradient Boosting models were trained and validated using cross-validation and ROC curves.
- Model performance was assessed using metrics such as AUC, Classification Accuracy, and F1 Score.
Main Results:
- Gradient Boosting demonstrated a balanced performance with an AUC of 0.72, Classification Accuracy of 0.41, and F1 Score of 0.32.
- Neural Networks achieved an AUC of 0.721, Classification Accuracy of 0.391, and F1 Score of 0.314.
- Naive Bayes showed lower performance with an AUC of 0.701, Classification Accuracy of 0.172, and F1 Score of 0.114.
- While no significant statistical differences were found, Gradient Boosting offered a more balanced precision-recall relationship.
Conclusions:
- In silico analysis using machine learning effectively predicted protein-coding mRNA sequences in P. gingivalis OMVs.
- Gradient Boosting emerged as a promising tool, outperforming Naive Bayes and Neural Networks in achieving balanced predictive performance.
- The findings suggest Gradient Boosting's potential as a reliable method for identifying protein-coding mRNA sequences in bacterial OMVs.
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