Computational Molecular Biology 2024, Vol.14, No.2, 84-94 http://bioscipublisher.com/index.php/cmb 92 SEMgraph, another tool, brings structural equation modeling (SEM) into the biological domain, offering a robust interface for modeling complex biological systems. This R package allows users to manage high-throughput data as multivariate networks and interpret causal effects with ease. Its user-friendly interface is designed for researchers who need scalable, reproducible, and interpretable results without the need for advanced coding expertise (Grassi et al., 2022). Additionally, CIMLA is a recently developed tool that enhances causal inference by integrating machine learning and feature attribution models to discover condition-dependent causal relationships in gene regulatory networks. CIMLA’s intuitive design allows researchers to focus on the biological interpretation of gene interactions while the tool handles computational complexity (Dibaeinia & Sinha, 2023). 7.3 Open-source platforms and resources Open-source platforms have become essential for ensuring broad access to cutting-edge causal inference tools. One such example is GReNaDIne, a Python-based library that provides users with a comprehensive set of 18 gene regulatory network inference methods. GReNaDIne allows users to preprocess RNA-seq and microarray data, perform causal inference, and combine outputs from multiple methods into robust ensemble models. The open-source nature of GReNaDIne makes it a valuable toolkit for the systems biology community, supporting reproducibility and collaboration through its GitLab repository (Schmitt et al., 2023). OpenMEE is another open-source platform tailored for meta-analysis and meta-regression, widely used in ecological and evolutionary biology. Its open-source, cross-platform design enables researchers to implement advanced statistical functionalities without requiring expertise in R programming, making it highly accessible to a broad range of researchers (Wallace et al., 2017). 8 Concluding Remarks This review has highlighted significant advances in the field of causal inference for biological network analysis. Causal inference methods, such as Bayesian networks, Granger causality, and Structural Equation Modeling (SEM), have proven invaluable in elucidating the complex relationships between genes, proteins, and other biological entities. The integration of causal inference with high-throughput data, especially from multi-omics platforms, allows researchers to uncover direct and indirect relationships that inform our understanding of disease mechanisms. These techniques have demonstrated their ability to provide insights into gene regulatory networks, signaling pathways, and disease progression. Additionally, software tools and computational platforms have become increasingly sophisticated, enabling the analysis of large-scale datasets and improving the accuracy of causal inferences in biological contexts. The future of causal inference in biological network analysis will likely involve continued integration with emerging technologies, such as single-cell sequencing and spatial transcriptomics. These technologies generate complex datasets that require new methods for causal inference capable of handling spatio-temporal dynamics and heterogeneous data sources. Further advances in machine learning and artificial intelligence, particularly in deep learning models, will also play a crucial role in refining causal inference approaches. Deep learning has the potential to uncover latent causal structures and provide more accurate predictions of gene interactions. Moreover, hybrid methods that combine experimental interventions, such as CRISPR-based gene editing, with computational inference are expected to provide more robust models of causality in biological systems. To advance the field of biological network analysis, researchers should prioritize the integration of multiple causal inference methods. This allows for more robust conclusions, particularly when analyzing complex diseases like cancer or neurodegenerative disorders. Bayesian networks, combined with methods like Mendelian randomization, should be employed to better understand the causal relationships between genes, proteins, and metabolites. Furthermore, the use of open-source tools and platforms such as GReNaDIne and PREMER should be encouraged to promote reproducibility and collaboration across the scientific community. Finally, there is a need for more user-friendly tools designed for biologists without computational expertise, as this will enhance the accessibility of causal inference methods and foster more widespread adoption in the research community.
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