Introduction:
Overview of Parkinson’s Disease and the significance of miRNA biomarkers. Aim: Develop a Java neural network for accurate classification of these biomarkers, integrating miRBase data via REST API. Project Objective:
Create a reliable, Java-based tool to aid in the early detection of Parkinson’s Disease through miRNA biomarker classification. Utilize REST API to access and process data from miRBase.
Data Source:
Primary Database: miRBase (http://www.mirbase.org/) Access Method: REST API queries (e.g., http://www.mirbase.org/…).
Tools and Libraries:
Neural Network Library: Deeplearning4j Data Processing: Apache Commons, Jackson JSON Processor REST API Development: Spring Boot, Jersey
Neural Network Development in Java:
Data Retrieval and Preprocessing:
Use Jersey client for REST API calls to miRBase. Utilize Jackson for JSON parsing. Clean data using Apache Commons.
Designing the Neural Network:
Implement a Multilayer Perceptron (MLP) using Deeplearning4j. Configure layers: Input layer (size based on miRNA features), hidden layers (experiment with 2-3 layers), output layer (2 neurons for classification).
Training the Model:
Develop feature space for miRNA custom data object (Sequence Descriptors, Genetic Target Descriptors). Split data into training and test sets (80/20 ratio). Train the model using backpropagation and stochastic gradient descent. Tune hyperparameters: learning rate, batch size, number of epochs.
Model Evaluation:
Use Deeplearning4j’s evaluation class to assess accuracy, precision, recall, and F1 score. Implement cross-validation for robustness.
REST API Integration:
Develop a Spring Boot application for REST API. Endpoint for model prediction: POST /classifyMiRNA Deploy locally or use cloud services like AWS for hosting.
Testing and Validation:
Perform validation using separate datasets. Collaborate with medical researchers for real-world data testing.
Conclusion:
Potential impact in medical diagnostics. Future enhancements: integrating additional databases, improving model accuracy.