An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data—Circumventing the p >> n Problem
Abstract
:1. Introduction
2. Results
2.1. Data Processing
2.2. The Optimal DL Architecture
2.3. The Classification Quality
2.4. Selection of the Best DL Architecture
2.5. Genomic and Functional Annotation
2.6. GWAS for Clinical Mastitis in Polish Holstein–Friesian Cows
3. Discussion
3.1. Data Processing
3.2. Functional Interpretation of Significant SNPs
4. Materials and Methods
4.1. Sequenced Animals
4.2. Genotyped Animals
4.3. Data Processing
4.4. Bioinformatic Pipeline
4.5. Statistical Pipeline
4.5.1. Logistic LASSO Regression
4.5.2. The Deep Learning Algorithm
4.5.3. Hyperparameter Tuning and Validation
4.5.4. The Estimation of the Optimal Cut-Off Point
4.5.5. The Selection of Significant SNPs
4.5.6. The Evaluation of DL Classifiers
- True positive (TP), defined as the scenario in which a mastitis-susceptible individual was classified as mastitis-susceptible.
- False positive (FP), defined as the scenario in which a mastitis-resistant individual was classified as mastitis-susceptible.
- True negative (TN), defined as the scenario in which a mastitis-resistant individual was classified as mastitis-resistant.
- False negative (FN), defined as the scenario in which a mastitis-susceptible individual was classified as mastitis-resistant.
4.6. Biological Pipeline
4.7. Genome-Wide Association Study for Clinical Mastitis in Genotyped Cows
- , with I being an identity matrix, representing the additive genetic variance component, and being equal to the number of SNPs (53,557);
- , where is the numerator relationship matrix calculated based on the pedigree relationship and is the rest of additive genetic variance that was not explained by SNPs ;
- where is an identity matrix and representing the residual variance.
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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N Layers | N Units Inside Each Layer | Dropout Rate within Each Layer | Learning Rate | |
---|---|---|---|---|
0.1 | 1 | [31] | [0.285] | 3.026 × 10−09 |
0.2 | 3 | [32; 36; 37] | [0.302; 0.218; 0.311] | 4.263 × 10−10 |
0.3 | 3 | [11; 16; 12] | [0.323; 0.242; 0.243] | 7.147 × 10−09 |
0.4 | 2 | [7; 46] | [0.210; 0.358] | 2.328 × 10−11 |
0.5 | 2 | [48; 45] | [0.312; 0.250] | 6.700 × 10−12 |
0.6 | 3 | [47; 37; 28] | [0.398; 0.278, 0.300] | 6.900 × 10−09 |
0.7 | 2 | [10; 18] | [0.215; 0.222] | 7.896 × 10−09 |
0.8 | 4 | [35; 35; 26; 13] | [0.323; 0.261, 0.257; 0.327] | 4.268 × 10−10 |
0.9 | 1 | [50] | [0.250] | 6.829 × 10−09 |
1.0 | 3 | [23; 49; 9] | [0.297; 0.362, 0.365] | 1.698 × 10−09 |
Sampled Hyperparameters | Range |
---|---|
Number of layers | [1, 6] |
Number of units per layer | [4, 50] |
Dropout rate | [0.2, 0.4] |
Learning rate | [1.0 × 10−12, 1.0 × 10−8] |
Fixed hyperparameters | |
Number of epochs | 300 |
Label smoothing | 0.2 |
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Kotlarz, K.; Mielczarek, M.; Biecek, P.; Wojdak-Maksymiec, K.; Suchocki, T.; Topolski, P.; Jagusiak, W.; Szyda, J. An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data—Circumventing the p >> n Problem. Int. J. Mol. Sci. 2024, 25, 4715. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms25094715
Kotlarz K, Mielczarek M, Biecek P, Wojdak-Maksymiec K, Suchocki T, Topolski P, Jagusiak W, Szyda J. An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data—Circumventing the p >> n Problem. International Journal of Molecular Sciences. 2024; 25(9):4715. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms25094715
Chicago/Turabian StyleKotlarz, Krzysztof, Magda Mielczarek, Przemysław Biecek, Katarzyna Wojdak-Maksymiec, Tomasz Suchocki, Piotr Topolski, Wojciech Jagusiak, and Joanna Szyda. 2024. "An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data—Circumventing the p >> n Problem" International Journal of Molecular Sciences 25, no. 9: 4715. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms25094715