Amino acid encoding for deep learning applications.

Area of Science:

• Bioinformatics
• Computational Biology
• Machine Learning

Background:

• Deep learning excels in bioinformatics, often outperforming classical methods with large datasets.
• Amino acids are typically represented by continuous vectors via embedding matrices in deep learning.
• End-to-end learning optimizes embeddings directly from data, yielding state-of-the-art results.

Purpose of the Study:

• Systematically evaluate end-to-end learning for single amino acid encoding.
• Compare end-to-end learned embeddings against classical encoding strategies (one-hot, VHSE8, BLOSUM62).
• Assess performance across different deep learning architectures (RNN, CNN, CNN-RNN).

Main Methods:

• Implemented and compared classical encoding matrices with end-to-end learned amino acid embeddings.
• Utilized recurrent neural networks (RNN), convolutional neural networks (CNN), and hybrid CNN-RNN architectures.
• Evaluated performance on two distinct bioinformatics prediction tasks.

Main Results:

• End-to-end learning achieved performance comparable to classical encodings, even with limited training data.
• A reduction in embedding dimension was possible without compromising performance, crucial for resource-constrained devices.
• Embedding dimension significantly impacts model performance; deep learning models can learn from random vectors.

Conclusions:

• End-to-end learning offers a flexible and potent method for amino acid encoding in bioinformatics.
• Benchmarking encoding schemes against random vectors is essential to differentiate information content from distinguishability effects.