| Category | Coursework | Subject | Nursing |
|---|---|---|---|
| University | ............ | Module Title | BIOL5148M Research Planning and Scientific Communication |
In this research sample we will analyze advanced machine learning techniques for genomic selection and compare them with traditional statistical methods. Genomic selection is a powerful approach used to predict genetic potential in breeding programs. The primary focus of this study is whether advanced machine learning methods, such as regularized regression, ensemble learning, and deep learning, are more accurate and computationally efficient than traditional statistical models.
We will use both synthetic (simulated) and real (empirical) datasets to check the robustness and reliability of machine learning models. In this we will analyze animal and plant datasets, with a special focus on the majority of datasets. During the data processing phase, phenotypic analysis and genotypic adjustments will be made to ensure that the data are unbiased and accurate.
For model evaluation, we will use performance metrics, which can objectively measure the effectiveness of machine learning and statistical models. We will systematically implement model training and prediction pipelines, to identify the best-performing approach for genomic selection.
1. Data Input or Collection
2. Data processing
Adjustment for Tester and Genotypic Group Effects:
3. Methodologies
4. Performance Evaluation
5. Model Training
6. Prediction
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