Computational Molecular Biology 2024, Vol.14, No.2, 84-94 http://bioscipublisher.com/index.php/cmb 89 different molecular layers (e.g., genomics, transcriptomics, and proteomics) to build comprehensive models of cellular function. SEM is also advantageous in situations where researchers wish to distinguish between direct effects (e.g., the effect of a transcription factor on gene expression) and indirect effects (e.g., the effect of a gene through an intermediate protein). Despite its strengths, SEM faces challenges, particularly in the requirement for large sample sizes to achieve reliable estimates, which can be a limitation in biological studies where obtaining large datasets may be difficult. Moreover, SEM models are sensitive to model misspecification, meaning that incorrect assumptions about the relationships between variables can lead to biased results. Nonetheless, with careful application and validation, SEM remains a versatile and powerful tool for uncovering causal relationships in biological systems (Lu et al., 2019). 5 Integration of Causal Inference with High-Throughput Data 5.1 Challenges with high-throughput data High-throughput technologies, such as next-generation sequencing (NGS), proteomics, and metabolomics, generate large-scale datasets that offer unprecedented opportunities to study biological systems. However, integrating these diverse datasets presents significant challenges. One major issue is the heterogeneity of omics data, where each data type (e.g., genomics, transcriptomics, epigenomics) has its own specific structure, scale, and distribution. For example, gene expression data are often continuous, while mutation data are categorical, complicating their integration. Additionally, high-throughput datasets are often noisy, incomplete, and subject to batch effects, which can obscure true biological signals and make causal inference more difficult. Another challenge is the sheer dimensionality of these datasets. With thousands of genes, proteins, and metabolites measured in each sample, the curse of dimensionality becomes a critical issue, as traditional statistical methods may become computationally intractable or prone to overfitting when applied to such large datasets (Miao et al., 2021). Moreover, integrating data across multiple platforms requires sophisticated techniques to normalize and harmonize the datasets, ensuring that the information is comparable across different data types. The complexity of biological networks, with their feedback loops and nonlinear interactions, adds another layer of difficulty, requiring advanced models capable of handling dynamic and multi-level interactions. 5.2 Approaches for data integration Various computational methods have been developed to address the challenges of integrating high-throughput omics data in causal inference. One common approach is multi-layered network modeling, where each omics dataset is treated as a separate layer of a larger network, and relationships between variables across layers are inferred using probabilistic models. This approach captures the hierarchical nature of biological systems, allowing for a more comprehensive understanding of genotype-phenotype associations and the environmental impact on organisms (Lee et al., 2020). Bayesian networks are another popular method, as they can integrate prior knowledge and probabilistically infer causal relationships while accounting for uncertainty. Machine learning methods, particularly deep learning models, have also been applied for data integration. These methods excel at extracting features from high-dimensional data and can capture complex, nonlinear relationships between variables. Techniques like matrix factorization and graph-based models have been used to reduce the dimensionality of multi-omics data and highlight the most important features for downstream causal analysis (Nicora et al., 2020). Additionally, causal inference techniques like Mendelian randomization are frequently employed, using genetic variants as instrumental variables to infer the causal effect of exposures on outcomes across multiple omics layers (Zhao, 2023). 5.3 Case studies in omics data The integration of causal inference with multi-omics data has shown considerable promise in precision medicine, particularly in oncology. For instance, the Omics Integrator tool has been used to combine transcriptomics, proteomics, and metabolomics data to reconstruct molecular networks involved in diseases such as glioblastoma and Huntington’s disease. This approach has identified novel pathways and key regulatory nodes that are
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