deepRAM

deepRAM is a comprehensive deep learning tool designed for predicting DNA—and RNA-binding specificity. It offers a wide range of deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid CNN/RNN models. The tool employs a fully automatic model selection procedure to provide an unbiased comparison of these architectures.

Key features of deepRAM:

1. Implements deep learning architectures for predicting DNA- and RNA-binding specificity.

2. Provides an end-to-end solution with a fully automatic model selection procedure, ensuring a fair comparison between architectures.

3. Demonstrates that deeper, more complex architectures perform better when sufficient training data is available.

4. Offers guidelines to assist users in selecting an appropriate network architecture for their specific needs.

5. Provides insights into the differences between the features learned by convolutional and recurrent networks.