Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins
Abstract
:1. Introduction
2. Material and Methods
2.1. Data Collection
2.2. Sequence Features
2.3. Network Features
2.4. Gene Ontology (GO) Features
2.5. Classification
2.5.1. Support Vector Machines (SVM)
2.5.2. Random Forest (RF)
2.5.3. Deep Neural Networks (DNN)
2.6. 10-Fold Cross-Validation
2.7. Feature Selection
2.8. Performance Measures
2.9. GO Enrichment Analysis
2.10. Pathway Enrichment Analysis
3. Results
3.1. Selection of Features
3.2. Performance Comparison of Different Classifiers
3.3. Gene Ontology Enrichment Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
References
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Sequence Features | ||||||
---|---|---|---|---|---|---|
Features Set | Vector Length | Method | Accuracy (%) | MCC | F1-Score (%) | AUC |
Amino acid composition (AAC) | 20 | SVM | 69.20 | 0.10 | 80.60 | 0.580 |
Amino acid composition (AAC) | 20 | RF | 70.20 | 0.05 | 82.30 | 0.629 |
Amino acid composition (AAC) | 20 | DNN | 71.90 | 0.21 | 82.30 | 0.699 |
Dipeptide composition (DC) | 400 | SVM | 70.10 | 0.09 | 81.90 | 0.598 |
Dipeptide composition (DC) | 400 | RF | 70.70 | 0.06 | 82.50 | 0.614 |
Dipeptide composition (DC) | 400 | DNN | 86.40 | 0.67 | 90.30 | 0.931 |
Pseudo-amino acid composition (PAAC) | 25 | SVM | 65.40 | 0.09 | 76.80 | 0.582 |
Pseudo-amino acid composition (PAAC) | 25 | RF | 70.70 | 0.09 | 82.30 | 0.628 |
Pseudo-amino acid composition (PAAC) | 25 | DNN | 71.00 | 0.19 | 81.30 | 0.708 |
Composition, Transition, and Distribution (CTD) | 147 | SVM | 71.00 | 0.01 | 83.00 | 0.525 |
Composition, Transition, and Distribution (CTD) | 147 | RF | 70.90 | 0.09 | 82.50 | 0.622 |
Composition, Transition, and Distribution (CTD) | 147 | DNN | 70.70 | 0.02 | 82.80 | 0.603 |
AAC_DC | 420 | SVM | 70.60 | 0.05 | 82.80 | 0.602 |
AAC_DC | 420 | RF | 70.50 | 0.06 | 82.50 | 0.620 |
AAC_DC | 420 | DNN | 86.00 | 0.66 | 90.00 | 0.924 |
AAC_DC_PAAC | 445 | SVM | 70.70 | 0.04 | 82.90 | 0.594 |
AAC_DC_PAAC | 445 | RF | 70.70 | 0.06 | 82.50 | 0.621 |
AAC_DC_PAAC | 445 | DNN | 92.40 | 0.81 | 94.90 | 0.939 |
AAC_DC_PAAC_CTD | 592 | SVM | 71.00 | 0.07 | 83.00 | 0.566 |
AAC_DC_PAAC_CTD | 592 | RF | 70.40 | 0.04 | 823.00 | 0.627 |
AAC_DC_PAAC_CTD | 592 | DNN | 70.10 | 0.03 | 83.00 | 0.588 |
Gene Ontology Features | ||||||
Gene Ontology (GO) | 282 | SVM | 52.60 | 0.03 | 61.40 | 0.283 |
Gene Ontology (GO) | 282 | RF | 66.70 | 0.13 | 77.90 | 0.613 |
Gene Ontology (GO) | 282 | DNN | 80.20 | 0.51 | 86.40 | 0.886 |
Network Features | ||||||
Network | 9 | SVM | 54.20 | 0.06 | 62.70 | 0.538 |
Network | 9 | RF | 53.90 | 0.06 | 62.60 | 0.527 |
Network | 9 | DNN | 53.30 | 0.05 | 61.90 | 0.512 |
Mixed features | ||||||
AAC_DC_PAAC_GO | 727 | SVM | 70.90 | 0.02 | 83.00 | 0.609 |
AAC_DC_PAAC_GO | 727 | RF | 70.50 | 0.05 | 82.30 | 0.635 |
AAC_DC_PAAC_GO | 727 | DNN | 83.40 | 0.60 | 88.30 | 0.914 |
AAC_DC_PAAC_CTD_GO | 874 | SVM | 71.00 | 0.06 | 83.00 | 0.567 |
AAC_DC_PAAC_CTD_GO | 874 | RF | 70.40 | 0.06 | 82.30 | 0.635 |
AAC_DC_PAAC_CTD_GO | 874 | DNN | 70.12 | 0.04 | 83.00 | 0.563 |
AAC_DC_PAAC_CTD_GO_Network | 883 | SVM | 70.30 | 0.06 | 81.50 | 0.595 |
AAC_DC_PAAC_CTD_GO_Network | 883 | RF | 70.50 | 0.07 | 82.60 | 0.642 |
AAC_DC_PAAC_CTD_GO_Network | 883 | DNN | 72.10 | 0.18 | 83.10 | 0.725 |
Features with Feature Selection Methods | Vector Length | Method | Accuracy (%) | MCC | F1-Score (%) | AUC |
---|---|---|---|---|---|---|
AAC_DC_PAAC_UFS_chi2 | 44 | SVM | 65.70 | 0.09 | 77.60 | 0.568 |
AAC_DC_PAAC_UFS_chi2 | 44 | RF | 70.40 | 0.08 | 82.30 | 0.634 |
AAC_DC_PAAC_UFS_chi2 | 44 | DNN | 71.90 | 0.21 | 82.10 | 0.704 |
AAC_DC_PAAC_UFS_f_classif | 44 | SVM | 64.80 | 0.08 | 76.40 | 0.550 |
AAC_DC_PAAC_UFS_f_classif | 44 | RF | 70.30 | 0.07 | 82.20 | 0.631 |
AAC_DC_PAAC_UFS_f_classif | 44 | DNN | 72.60 | 0.22 | 82.60 | 0.705 |
AAC_DC_PAAC_UFS_mutual_info_classif | 44 | SVM | 62.20 | 0.14 | 71.90 | 0.604 |
AAC_DC_PAAC_UFS_mutual_info_classif | 44 | RF | 70.30 | 0.06 | 82.40 | 0.622 |
AAC_DC_PAAC_UFS_mutual_info_classif | 44 | DNN | 72.10 | 0.23 | 82.00 | 0.714 |
AAC_DC_PAAC_RFE | 44 | SVM | 69.90 | 0.08 | 81.50 | 0.584 |
AAC_DC_PAAC_RFE | 44 | RF | 70.20 | 0.06 | 82.30 | 0.633 |
AAC_DC_PAAC_RFE | 44 | DNN | 73.10 | 0.25 | 83.00 | 0.716 |
AAC_DC_PAAC_SFM | 376 | SVM | 70.60 | 0.04 | 82.80 | 0.595 |
AAC_DC_PAAC_SFM | 376 | RF | 70.60 | 0.06 | 82.40 | 0.627 |
AAC_DC_PAAC_SFM | 376 | DNN | 75.10 | 0.39 | 82.60 | 0.796 |
AAC_DC_PAAC_TBFS | 227 | SVM | 70.30 | 0.07 | 82.30 | 0.604 |
AAC_DC_PAAC_TBFS | 227 | RF | 70.60 | 0.06 | 82.40 | 0.628 |
AAC_DC_PAAC_TBFS | 227 | DNN | 77.00 | 0.44 | 84.00 | 0.805 |
Term | p-Value |
---|---|
regulation of nucleic acid-templated transcription (GO:1903506) | 0.003414 |
regulation of cellular macromolecule biosynthetic process (GO:2000112) | 0.004395 |
negative regulation of catalytic activity (GO:0043086) | 0.008008 |
glomerulus vasculature development (GO:0072012) | 0.029631 |
regulation of relaxation of cardiac muscle (GO:1901897) | 0.029631 |
dosage compensation by inactivation of X chromosome (GO:0009048) | 0.029631 |
negative regulation of cellular response to hypoxia (GO:1900038) | 0.029631 |
pronephros development (GO:0048793) | 0.029631 |
negative regulation of cellular catabolic process (GO:0031330) | 0.033057 |
negative regulation of nitric oxide biosynthetic process (GO:0045019) | 0.034484 |
negative regulation of nitric oxide metabolic process (GO:1904406) | 0.034484 |
negative regulation of calcium ion import (GO:0090281) | 0.034484 |
negative regulation of RIG-I signaling pathway (GO:0039536) | 0.034484 |
glycosphingolipid catabolic process (GO:0046479) | 0.034484 |
thiamine-containing compound metabolic process (GO:0042723) | 0.034484 |
regulation of cardiac muscle cell membrane potential (GO:0086036) | 0.034484 |
negative regulation of cell adhesion mediated by integrin (GO:0033629) | 0.034484 |
positive regulation of histone H4 acetylation (GO:0090240) | 0.034484 |
negative regulation of heart rate (GO:0010459) | 0.034484 |
regulation of relaxation of muscle (GO:1901077) | 0.034484 |
Term | p-Value |
---|---|
peptide biosynthetic process (GO:0043043) | 8.36 × 10−11 |
translation (GO:0006412) | 2.25 × 10−9 |
mitochondrial ATP synthesis-coupled electron transport (GO:0042775) | 2.63 × 10−7 |
mitochondrial translational elongation (GO:0070125) | 3.08 × 10−7 |
cellular macromolecule biosynthetic process (GO:0034645) | 3.45 × 10−7 |
mitochondrial translational termination (GO:0070126) | 3.60 × 10−7 |
respiratory electron transport chain (GO:0022904) | 5.21 × 10−7 |
translational termination (GO:0006415) | 6.01 × 10−7 |
translational elongation (GO:0006414) | 1.10 × 10−6 |
gene expression (GO:0010467) | 1.14 × 10−6 |
mitochondrial translation (GO:0032543) | 1.25 × 10−6 |
mitochondrial electron transport, cytochrome c to oxygen (GO:0006123) | 3.43 × 10−6 |
epidermis development (GO:0008544) | 3.67 × 10−6 |
cellular protein metabolic process (GO:0044267) | 6.09 × 10−6 |
protein targeting to ER (GO:0045047) | 1.03 × 10−5 |
intermediate filament organization (GO:0045109) | 4.37 × 10−5 |
SRP-dependent cotranslational protein targeting to membrane (GO:0006614) | 9.30 × 10−5 |
peptide cross-linking (GO:0018149) | 1.01 × 10−4 |
cotranslational protein targeting to membrane (GO:0006613) | 1.14 × 10−4 |
skin development (GO:0043588) | 1.20 × 10−4 |
Pathway Name | Entities p Value |
---|---|
Uptake and function of anthrax toxins | 0.009407594 |
ARL13B-mediated ciliary trafficking of INPP5E | 0.02672975 |
Defective NEU1 causes sialidosis | 0.02672975 |
Vitamin B1 (thiamin) metabolism | 0.044155321 |
RUNX1 interacts with cofactors whose precise effect on RUNX1 targets is not known | 0.044545497 |
Pathway Name | Entities p Value |
---|---|
Formation of the cornified envelope | 1.78 × 10−10 |
Keratinization | 9.88 × 10−9 |
Translation | 3.97 × 10−7 |
Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins. | 1.94 × 10−6 |
Mitochondrial translation termination | 1.58 × 10−5 |
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Barman, R.K.; Mukhopadhyay, A.; Maulik, U.; Das, S. Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins. Processes 2022, 10, 291. https://doi.org/10.3390/pr10020291
Barman RK, Mukhopadhyay A, Maulik U, Das S. Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins. Processes. 2022; 10(2):291. https://doi.org/10.3390/pr10020291
Chicago/Turabian StyleBarman, Ranjan Kumar, Anirban Mukhopadhyay, Ujjwal Maulik, and Santasabuj Das. 2022. "Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins" Processes 10, no. 2: 291. https://doi.org/10.3390/pr10020291
APA StyleBarman, R. K., Mukhopadhyay, A., Maulik, U., & Das, S. (2022). Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins. Processes, 10(2), 291. https://doi.org/10.3390/pr10020291