Hybrid Predictive Machine Learning Model for the Prediction of Immunodominant Peptides of Respiratory Syncytial Virus
<p>Structure of RSV.</p> "> Figure 2
<p>Proposed methodology.</p> "> Figure 3
<p>K-fold cross-validation technique.</p> "> Figure 4
<p>KFCV results of the hybrid model as depicted in <a href="#bioengineering-11-00791-f004" class="html-fig">Figure 4</a>; it is evident that the hybrid XGBoost model exhibits the most consistent accuracy results compared to the RF hybrid techniques. The results indicate that the proposed model stands out due to its comprehensive hybrid framework that combines multiple feature weighting, selection, and classification techniques, aiming to capture diverse peptide characteristics for improved accuracy. Unlike many existing tools relying on single models or limited feature engineering, the proposed approach leverages the strengths of different algorithms to mitigate potential biases. Moreover, the KFCV technique mitigates the risk of overfitting by dividing the dataset into K equal-sized folds. The model is trained on K-1 folds and tested on the remaining fold iteratively. This process provides a more reliable estimate of model performance on unseen data by exposing the model to different subsets of the data during training.</p> ">
Abstract
:1. Introduction
Contributions
2. Related Work
3. Materials and Methods
3.1. Retrieval of Peptide Sequences
3.2. Feature Extraction
3.3. Feature Selection
3.4. Assigning Weights to Features-Feature Weighting
3.4.1. IGT
- IG(T,A) is the information gain of feature A for target T.
- H(T) is the entropy of the target variable T.
- H(T∣A) is the conditional entropy of T given feature A.
3.4.2. ChST
- Oi is the observed frequency for category i.
- Ei is the expected frequency for category i, assuming no association between the feature and the target.
3.5. Selection of Optimal Subset of Features
3.5.1. HCST
3.5.2. BST
3.6. Selection of ML Classifiers
3.7. Model Building
4. Model Evaluation
4.1. Accuracy
4.2. Sensitivity
4.3. Specificity
4.4. Precision
4.5. F1 Score
4.6. Area under the ROC Curve (AUC-ROC)
4.7. Mathews Correlation Coefficient (MCC)
4.8. K-Fold Cross Validation (KFCV)
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Physicochemical Property | Count | Notation |
---|---|---|
Aliphatic index | 1 | F1 |
Boman index | 1 | F2 |
Insta index | 1 | F3 |
Probability of detection | 1 | F4 |
Hmoment index | 2 | F5_1, F5_2 |
Molecular weight | 2 | F6_1, F6_2 |
Peptide charge for 45 scales | 45 | F7_1 to F7_45 |
Hydrophobicity at 44 scales | 44 | F8_1 to F8_44 |
Isoelectric point for 9 pKscale | 9 | F9_1 to F9_9 |
Kidera factors | 1 | F10 |
aaComp | 1 | F11 |
Peptide Sequence | F1 | F2 | ----- | F10 | F11 | Class |
---|---|---|---|---|---|---|
VRSKVF | 28.10 | 0.976 | ----- | −2.765 | 4.101 | 1 |
SRISKDAT | 34.76 | 0.409 | ----- | −1.98 | −1.342 | 1 |
KFELRZFIG | 132.60 | 2.157 | ----- | −0.577 | −0.029 | 0 |
SAVFEKTLS | 97 | −6.98 | ----- | −0.912 | −4.719 | 0 |
Classifier | Method | Package | Tuning Parameter |
---|---|---|---|
XGB | xgb | xgboost | (booster = “gbtree”, objective = “binary:logistic”, max_depth = 6, min_child_weight = 1, subsample = 1) |
RF | randomForest | randomForest | Ntree = 1500, mtry = 10 |
Technique | Optimal Feature Set | No. of Features |
---|---|---|
HcS | F7_43, F7_42, F1, F8_43, F7_38, F11_7, F7_28, F8_24, F4, F7_41, F8_13, F7_6, F7_22, F10, F8_23, F7_7, F7_19, F8_16, F9_2, F7_23, F5_2, F11_3 | 22 |
BST | F6_1, F9_4, F9_7, F7_8, F8_7, F7_37, F7_1, F5_2, F8_10, F3, F7_40, F7_39, F7_24, F8_19, F7_5, F1, F7_33, F8_20, F7_34, F7_38, F2, F9_5, F7_14, F11_13, F6_2 | 25 |
FWT | OFSS | CT | |
---|---|---|---|
BST | HCST | ||
IGT | 93.65 | 95.12 | |
ChST | 97.10 | 95.64 | XGB |
IGT | 79.23 | 94.19 | |
ChST | 91.32 | 93.68 | RF |
Model | Sensitivity | Specificity | F1 Score | AUC | Precision | MCC |
---|---|---|---|---|---|---|
Model 1: ChST−BST–XGB | 0.98 | 0.97 | 0.98 | 0.99 | 0.99 | 0.96 |
Model 2: IGT−HCST–RF | 0.92 | 0.93 | 0.90 | 0.96 | 0.92 | 0.94 |
Run | Model 1 | Model 2 |
---|---|---|
1 | 97.45 | 92.76 |
2 | 97.31 | 94.11 |
3 | 95.78 | 95.19 |
4 | 97.62 | 94.43 |
5 | 97.92 | 93.96 |
Average Accuracy | 97.216 | 94.09 |
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Bukhari, S.N.H.; Ogudo, K.A. Hybrid Predictive Machine Learning Model for the Prediction of Immunodominant Peptides of Respiratory Syncytial Virus. Bioengineering 2024, 11, 791. https://doi.org/10.3390/bioengineering11080791
Bukhari SNH, Ogudo KA. Hybrid Predictive Machine Learning Model for the Prediction of Immunodominant Peptides of Respiratory Syncytial Virus. Bioengineering. 2024; 11(8):791. https://doi.org/10.3390/bioengineering11080791
Chicago/Turabian StyleBukhari, Syed Nisar Hussain, and Kingsley A. Ogudo. 2024. "Hybrid Predictive Machine Learning Model for the Prediction of Immunodominant Peptides of Respiratory Syncytial Virus" Bioengineering 11, no. 8: 791. https://doi.org/10.3390/bioengineering11080791