d) <strong> Hybrid algorithm </strong> is the most optimal choice, since simple classification/regression models can be "inversed" using evolutionary algorithms. Moreover, results obtained by simpler models can be reused by more complex to compensate for insufficient data.
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<h4><ins>2. Create a database</ins>. Process datasets, look for more data, merge it, clean, and unify, create a database with DBMS. </h4>
- study the organization of data in the datasets
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- move the data to DBMS
- set up access, data retrieval etc.
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<h4><ins>3. Analyze the data</ins>. Perform sequence alignment, look for conservative patterns, study correlations. </h4>
- perform local or global sequence alignment on CPP and non-CPP sequences (either all or particular groups/clusters)
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- make correlation plots for categorical and numeric parameters
- try to answer the question what parameters and sequence patterns lead to best-performing CPPs
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<h4><ins>4. Choose the models</ins>. Find the best-performing classification/regression/generation models to develop and compare. </h4>
- do not screen models which were shown to underperform most of the modern ML/DL models
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- you can use models pre-trained on more abundant data (transfer learning)
- prioritize interpretable models over black-box
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<h4><ins>5. Build and optimize the models</ins>. Check model performance on default parameters, optimize hyperparameters and architechture. </h4>
- choose the logic of train/test split (random, stratified, rule-based etc.)
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- make a list of architectures you want to test (for neural networks)
- choose a method for hyperparameter tuning (Optuna, Grid search etc.)
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<h4><ins>6. Choose the best-performing model</ins>. Prioritize the list of models by accuracy, interpretability, extrapolative power, and robustness. </h4>
- analyze model performance (use appropriate classification/regression metrics or loss functions)
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- analyze model speed
- choose the best model according to these parameters
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<h4><ins>7. Validate your model</ins>. Use computational, predictive, or hybrid approaches to check model consistency with first principles and previous studies. </h4>
- choose the methods for additional model validation (computational models, benchmarked ML/DL models, hybrid approaches)
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- analyze how good these methods explain obtained results
- generate novel CPPs for validation
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<h4><ins>8. Formalize the results</ins>. Create a repo, systematize analysis results, submit the code, ensure the code is reproducible, usable, and readable. </h4>
- create a GitHub repository structure
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- uptake type,
- sequence.
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<h3> 2. Natural CPPs </h3>
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Represents a balanced dataset of CPPs and non-CPPs; often used for model benchmarking.
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<h3> 3. Non-CPPs </h3>
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Contains non-CPP sequences shown not to demonstrate activity experimentally.
ACID Design Lab
7 months ago
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