An overview of topic modeling and its current applications in bioinformatics

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Increasing biological datasets necessitate automated analysis methods.
• Topic models from natural language processing are gaining traction in bioinformatics due to their interpretability.
• This review focuses on the application and development of topic models for bioinformatics.

Purpose of the Study:

• To review the application and development of topic models in bioinformatics.
• To provide an understanding of topic modeling principles.
• To categorize existing studies and offer an outlook on future applications.

Main Methods:

• Described topic modeling concepts and application development.
• Conducted a literature search and in-depth analysis of topic model applications in biological data.
• Categorized studies based on model types and biological data analogies.

Main Results:

• Topic modeling provides an interpretable alternative to traditional data reduction methods in bioinformatics.
• Identified a need for topic models optimized for specific biological data types.
• Topic models show promise for diverse bioinformatics applications.

Conclusions:

• Topic modeling enhances biological information interpretation.
• Further development is needed for specialized biological data topic models.
• Topic models represent a promising avenue for bioinformatics research.