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.