RNAAlpha is a deep learning model designed for predicting RNA 3D structures and folding patterns. Developed by WolfTech Innovations, this model leverages convolutional layers, transformers, and LSTMs to enhance RNA structure prediction accuracy.
- RNA 3D Structure Prediction: Uses a transformer-based approach for precise 3D coordinate predictions.
- RNA Folding Prediction: Incorporates an LSTM-based module to predict base-pair probabilities.
- One-Hot Encoding: Converts RNA sequences into a format suitable for deep learning.
- Synthetic Data Generation: Creates simulated RNA sequences and structures for training.
- Efficient Training: Implements AdamW optimizer with Mean Squared Error (MSE) and Binary Cross-Entropy Loss (BCE) for training stability.
RNAAlpha consists of the following components:
- Convolutional Layer: Extracts feature representations from RNA sequences.
- Transformer Encoder: Captures complex dependencies within RNA structures.
- Fully Connected Layers: Maps encoded features to 3D coordinates.
- LSTM Module: Predicts RNA folding probabilities using bidirectional LSTMs.
Ensure you have Python installed along with the following dependencies:
pip install numpy torch scikit-learnTo train RNAAlpha on synthetic data:
python train_model.pyTo use a pre-trained RNAAlpha model:
import torch
from RNAAlpha import RNAAlpha3DModel
model = RNAAlpha3DModel(seq_length=100)
model.load_state_dict(torch.load("RNAAlpha_Model.pth"))
model.eval()- Dataset: Simulated RNA sequences with 3D coordinates and folding labels.
- Batch Size: 64
- Learning Rate: 1e-4
- Weight Decay: 5e-5
- Epochs: 30
RNAAlpha achieves high accuracy in predicting RNA structures and folding patterns, making it suitable for bioinformatics and structural biology research.
RNAAlpha is developed by WolfTech Innovations, and released under the GPL2 License
For inquiries or contributions, contact WolfTech Innovations at spoinkosgithub@gmail.com.