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Updated: Feb 10, 2026
09:34
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
Published on: September 25, 2021
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Applications of Deep Learning and Reinforcement Learning to Biological Data
Summary
Recent hardware advances enable life scientists to collect complex multimodal biological data. This survey explores how deep learning (DL) and reinforcement learning (RL) are revolutionizing biological data mining and analysis.
Area of Science:
- Computational Biology
- Bioinformatics
- Artificial Intelligence in Life Sciences
Background:
- Hardware advancements facilitate multimodal data acquisition in life sciences (e.g., omics, bioimaging).
- Increasing computational power and data storage enable sophisticated data-intensive machine learning techniques.
- Deep learning (DL), reinforcement learning (RL), and deep RL show promise for revolutionizing artificial intelligence in scientific research.
Purpose of the Study:
- To provide a comprehensive survey on the application of DL, RL, and deep RL in biological data mining.
- To compare the performance of DL techniques across diverse datasets and application domains.
- To identify open challenges and future perspectives in this research area.
Main Methods:
- Literature review and synthesis of research applying DL, RL, and deep RL to biological data.
- Comparative analysis of DL technique performance on various biological datasets.
- Identification and discussion of current limitations and future research directions.
Main Results:
- DL, RL, and deep RL are increasingly applied to complex biological datasets, overcoming previous limitations.
- Performance of DL techniques varies across different biological data types and applications.
- Significant potential exists for these AI methods to advance biological data analysis.
Conclusions:
- DL, RL, and deep RL are powerful tools for mining complex biological data, driven by technological progress.
- Further research is needed to address open issues and fully realize the potential of AI in life sciences.
- This survey highlights the transformative impact and future trajectory of AI in biological data mining.
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