Enhancing Missense Variant Pathogenicity Prediction with MissenseNet: Integrating Structural Insights and ShuffleNet-Based Deep Learning Techniques
Abstract
:1. Introduction
2. Feature Extractions
2.1. Commonly Used Predictive Features
2.2. AlphFold2 Structural Features
3. Data Processing
3.1. Data Set Composition
3.2. Data Acquisition
3.3. Data Imbalance Treatment
4. Construction of the Model
4.1. ShuffleNet
4.2. Squeeze-and-Excitation Module
4.3. Propose the Model MissenseNet
5. Model Evaluation Index
6. Results
6.1. Feature Ablation Experiments
6.2. Comparison with Commonly Used Machine Learning Models
6.3. Comparison with Typical Deep Learning Models
6.4. Results of Ablation Experiments with SE and Encoder-Decoder Modules
6.5. Comparison with Single-Type Forecasting Tools
6.6. Comparison with Integrated Forecasting Tools
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Single-type forecasting tool | Sequence-based prediction methods (A) | SIFT [18] |
SIFT4GSIF [28] | ||
PROVEAN [27] | ||
GERP++ [30] | ||
phyloP100way [31] | ||
phyloP30way [32] | ||
SiPhy [33] | ||
Methods integrating structural and functional features (B) | Polyphen2_HDIV [14] | |
Polyphen2_HVAR [14] | ||
MutationTaster2 [34] | ||
VEST4 [35] | ||
Fathmm [36] | ||
GenoCanyon [37] | ||
29Ensembletools (C) | REVEL [38] | |
CADD [39] | ||
DANN [40] | ||
MetaSVM [41] | ||
MetaLR [41] | ||
DEOGEN2 [42] | ||
PrimateAI [43] | ||
Eigen [44] |
Feature | Accuracy | Recall | Precision | F1 Score | MCC | Balance ACC | AUC | AUC-PR |
---|---|---|---|---|---|---|---|---|
A | 0.8436 | 0.7938 | 0.7930 | 0.7905 | 0.6690 | 0.8335 | 0.9167 | 0.8554 |
B | 0.8868 | 0.8717 | 0.8343 | 0.8516 | 0.7618 | 0.8837 | 0.9474 | 0.9148 |
C | 0.9048 | 0.8762 | 0.8715 | 0.8727 | 0.7980 | 0.8990 | 0.9625 | 0.9411 |
D | 0.7053 | 0.4943 | 0.6736 | 0.5302 | 0.3523 | 0.6626 | 0.7834 | 0.6806 |
Feature | Accuracy | Recall | Precision | F1 Score | MCC | Balance ACC | AUC | AUC-PR |
---|---|---|---|---|---|---|---|---|
A + D | 0.8543 | 0.8306 | 0.7940 | 0.8121 | 0.8095 | 0.8495 | 0.9306 | 0.8831 |
B + D | 0.8898 | 0.8867 | 0.8305 | 0.8571 | 0.7694 | 0.8892 | 0.9552 | 0.9265 |
C + D | 0.9101 | 0.8773 | 0.8841 | 0.8792 | 0.8095 | 0.9035 | 0.9655 | 0.9461 |
Feature | Accuracy | Recall | Precision | F1 Score | MCC | Balance ACC | AUC | AUC-PR |
---|---|---|---|---|---|---|---|---|
A | 0.8436 | 0.7938 | 0.7930 | 0.7905 | 0.6690 | 0.8335 | 0.9167 | 0.8554 |
A + B | 0.8886 | 0.8306 | 0.8668 | 0.8471 | 0.7613 | 0.8768 | 0.9528 | 0.9191 |
A + B + C | 0.9126 | 0.8945 | 0.8762 | 0.8843 | 0.8154 | 0.9089 | 0.9676 | 0.9492 |
A + B + C + D | 0.9182 | 0.8776 | 0.9020 | 0.8889 | 0.8253 | 0.9100 | 0.9701 | 0.9549 |
Model | Accuracy | Recall | Precision | F1 Score | MCC | Balance ACC | AUC | AUC-PR |
---|---|---|---|---|---|---|---|---|
RF | 0.9178 | 0.8804 | 0.8916 | 0.8896 | 0.6741 | 0.9118 | 0.9686 | 0.9506 |
SVM | 0.7528 | 0.5807 | 0.7055 | 0.6368 | 0.4573 | 0.7181 | 08465 | 0.7480 |
LR | 0.9160 | 0.8899 | 0.8861 | 0.8877 | 0.8209 | 0.9107 | 0.9692 | 0.9523 |
DT | 0.8705 | 0.8301 | 0.8245 | 0.8271 | 0.7238 | 0.8623 | 0.8623 | 0.7479 |
KNN | 0.7954 | 0.7245 | 0.7269 | 0.7255 | 0.5628 | 0.7811 | 0.8586 | 0.7401 |
MissenseNet | 0.9182 | 0.8776 | 0.9020 | 0.8889 | 0.8253 | 0.9100 | 0.9701 | 0.9549 |
Model | Accuracy | Recall | Precision | F1 Score | MCC | Balance ACC | AUC | AUC-PR |
---|---|---|---|---|---|---|---|---|
MLP | 0.9009 | 0.8682 | 0.8724 | 0.8673 | 0.7919 | 0.8943 | 0.9658 | 0.9466 |
RNN | 0.8073 | 0.7254 | 0.7771 | 0.7422 | 0.5986 | 0.7908 | 0.8739 | 0.8239 |
CNN | 0.9061 | 0.8719 | 0.8815 | 0.8740 | 0.8025 | 0.8992 | 0.9666 | 0.9477 |
DenseNet | 0.8607 | 0.8463 | 0.8374 | 0.8288 | 0.7316 | 0.8577 | 0.9594 | 0.9361 |
ResNet | 0.8756 | 0.7715 | 0.8918 | 0.8145 | 0.7382 | 0.8546 | 0.9605 | 0.9379 |
MissenseNet | 0.9182 | 0.8776 | 0.9020 | 0.8889 | 0.8253 | 0.9100 | 0.9701 | 0.9549 |
Model | Factor | Acc | Recall | Precision | F1 Score | AUC | AUC-PR | |
---|---|---|---|---|---|---|---|---|
SE Module | Encoder-Decoder Module | |||||||
MissenseNet (Baseline) | / | / | 0.8981 | 0.8459 | 0.8892 | 0.8606 | 0.9683 | 0.9512 |
MissenseNet-(EN) | / | √ | 0.9167 | 0.8714 | 0.9034 | 0.8864 | 0.9700 | 0.9540 |
MissenseNet (SE + EN) | √ | √ | 0.9182 | 0.8776 | 0.9020 | 0.8889 | 0.9701 | 0.9549 |
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Liu, J.; Chen, Y.; Huang, K.; Guan, X. Enhancing Missense Variant Pathogenicity Prediction with MissenseNet: Integrating Structural Insights and ShuffleNet-Based Deep Learning Techniques. Biomolecules 2024, 14, 1105. https://doi.org/10.3390/biom14091105
Liu J, Chen Y, Huang K, Guan X. Enhancing Missense Variant Pathogenicity Prediction with MissenseNet: Integrating Structural Insights and ShuffleNet-Based Deep Learning Techniques. Biomolecules. 2024; 14(9):1105. https://doi.org/10.3390/biom14091105
Chicago/Turabian StyleLiu, Jing, Yingying Chen, Kai Huang, and Xiao Guan. 2024. "Enhancing Missense Variant Pathogenicity Prediction with MissenseNet: Integrating Structural Insights and ShuffleNet-Based Deep Learning Techniques" Biomolecules 14, no. 9: 1105. https://doi.org/10.3390/biom14091105