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datasetEngine
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datasetEngine
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1. Data Source Selection
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2. Data Retrieval from Sources (APIs, Downloads, etc.)
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3. Data Preprocessing
|--- Genomic Datasets
| |--- Parse FASTA and BED files
| |--- Extract gene sequences and annotations
| |--- Quality control (sequence validity, annotation consistency)
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|--- Medical Datasets
| |--- Extract relevant patient and clinical data
| |--- Normalize and anonymize data
| |--- Handle missing data and outliers
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|--- Molecular Information
|--- Extract molecular structures
|--- Convert SMILES/InChI to suitable representations
|--- Handle missing or incomplete molecular data
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4. Data Integration and Fusion
|--- Match and merge data across sources (e.g., gene IDs)
|--- Link molecular information with genetic and clinical data
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5. Feature Engineering
|--- Generate derived features (e.g., sequence motifs, structural properties)
|--- Embed categorical data (e.g., gene types, medical diagnoses)
|--- Normalize continuous data (e.g., gene expression levels, clinical measurements)
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6. Data Splitting
|--- Divide data into training, validation, and testing sets
|--- Ensure balanced distribution of classes and conditions
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7. Data Format Conversion
|--- Convert data into appropriate formats for model input
|--- Represent sequences as numerical tensors
|--- Transform molecular structures into graph or matrix representations
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8. Model Training and Validation
|--- Train the hybrid neural network model
|--- Validate model using cross-validation and evaluation metrics
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9. Iterative Optimization
|--- Analyze model performance and errors
|--- Adjust preprocessing steps and model architecture
|--- Fine-tune hyperparameters based on validation results
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10. Model Deployment
|--- Deploy the trained model in a suitable environment
|--- Integrate with user interface or application
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11. Real-world Application
|--- Provide input data (genomic, medical, molecular)
|--- Model generates RNA-based therapeutic sequences
|--- Validate therapeutic candidates using experimental data
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12. Feedback Loop
|--- Collect user feedback on generated therapeutics
|--- Incorporate user feedback into model improvement
|--- Update and retrain the model periodically