CausalMGM: an interactive web-based causal discovery tool.

Area of Science:

• Systems biology
• Bioinformatics
• Computational biology

Background:

• High-throughput sequencing and large data repositories enable multi-modal data integration.
• Current data mining methods identify correlations, not causation, limiting biological insights.
• Causal discovery algorithms infer cause-and-effect from observational data but are complex for non-experts.

Purpose of the Study:

• To introduce CausalMGM, the first web-based tool for causal discovery from observational biomedical data.
• To provide accessible causal inference methods for systems biology researchers.
• To facilitate the identification of cause-and-effect relationships in complex datasets.

Main Methods:

• Development of a web-based platform, CausalMGM (http://causalmgm.org/).
• Integration of three core tools: feature selection/clustering, graphical model-based causal discovery, and causal graph visualization.
• Demonstration of end-to-end exploratory data analysis for biomedical datasets.

Main Results:

• CausalMGM provides an accessible interface for non-experts to perform causal discovery.
• The tool enables automated identification of cause-and-effect relationships using graphical models.
• Interactive visualization of learned causal graphs enhances understanding of data interactions.

Conclusions:

• CausalMGM empowers researchers to explore cause-and-effect relationships in biomedical data.
• The web-based tool democratizes causal discovery, advancing systems biology research.
• CausalMGM offers a clear pathway for end-to-end exploratory analysis of multi-modal datasets.