Deep learning in bioinformatics: Introduction, application, and perspective in the big data era.

Area of Science:

• Bioinformatics
• Computational Biology
• Machine Learning

Background:

• The big data era in biology necessitates advanced computational tools.
• Deep learning excels at handling large, complex datasets.
• Deep learning is increasingly vital for bioinformatics analysis pipelines.

Purpose of the Study:

• To provide an accessible introduction to deep learning for bioinformatics.
• To showcase representative deep learning applications and implementations in the field.
• To address common challenges and offer solutions for adopting deep learning in bioinformatics.

Main Methods:

• Review of deep learning architectures (e.g., CNNs, RNNs, GNNs, GANs, VAEs).
• Presentation of eight practical examples across five bioinformatics research directions and four data types.
• Implementation details using Tensorflow and Keras.

Main Results:

• Demonstration of deep learning's suitability for various bioinformatics problems.
• Practical code examples for implementing deep learning models.
• Identification of common issues like overfitting and interpretability.

Conclusions:

• Deep learning is a powerful and increasingly indispensable tool in modern bioinformatics.
• Practical implementation guides and discussion of challenges facilitate adoption.
• Freely available code supports further research and application.