Kaiwen Liang , Jiefu Lin

Hainan Key Laboratory of Crop Molecular Breeding, Sanya, 572025, Hainan, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 2   doi: 10.5376/cmb.2024.14.0010
Received: 20 Feb., 2024    Accepted: 01 Apr., 2024    Published: 21 Apr., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Lin J.F., and Liang K.W., 2024, Advances in causal inference methods for biological network analysis, Computational Molecular Biology, 14(2): 84-94 (doi: 10.5376/cmb.2024.14.0010)

This study summarizes various causal inference methods for biological network analysis, including Bayesian networks, Granger causality, and structural equation modeling (SEM). We explored the application of these methods in integrating multiple omics data and how to overcome the challenges posed by high-dimensional data. Especially, the application of causal inference in disease network analysis demonstrates its potential in identifying key genes, revealing disease mechanisms, and promoting precision medicine. We also evaluated the latest developed computing tools and open-source platforms, which make large-scale data processing more efficient and user-friendly. In the future, the development of causal inference will further rely on the integration of emerging technologies such as machine learning and single-cell omics to promote a deeper understanding of complex disease mechanisms.

Causal inference; Bayesian networks; Granger causality; Structural equation modeling; Gene regulatory networks