Computational Molecular Biology 2025, Vol.15, No.5, 235-244 http://bioscipublisher.com/index.php/cmb 237 mammary specific genes, expression QTL, and splicing QTL in order to possibly improve the prediction accuracy (Križanac et al., 2025). 3.2 Transcriptomic data (mRNA, lncRNA, miRNA) When it comes to the gene regulation related to lactation, relying solely on genomic information is clearly insufficient. At the transcriptome level, mRNA is undoubtedly the main character, but non-coding molecules such as lncRNA and miRNA should not be ignored either. Between high-yield dairy cows and low-yield dairy cows, differentially expressed genes (DEGs) are often enriched in pathways such as immunity, metabolism, and mammary gland function (Nguyen, 2025). Some specific lncrnas and mirnas are also considered to possibly be key nodes regulating milk fat and milk protein synthesis (Shin et al., 2025). Interestingly, the expression patterns of these non-coding Rnas often coincide with GWAS signals and QTL regions, which also indicates that they play a more important role in the regulation of milk production traits than imagined. 3.3 Functional information from epigenomic, metabolomic, and proteomic data Not all variations related to milk production are written on the DNA sequence; some "hidden" regulations come from the epigenetic level. For instance, epigenetic markers such as DNA methylation or histone modifications (like H3K27ac, H3K4Me1) can be associated with milk production traits in breast tissue (Figure 1) (Dong et al., 2021; Cai et al., 2025). When it comes to the metabolome, milk production is essentially an energy-intensive process. The changes in metabolites along the energy metabolism or amino acid synthesis pathways often vary significantly between high-yield and low-yield dairy cows. Proteomics is similar. It can reveal the key proteins involved in lactation and even the signals of certain post-translational modifications. These seemingly scattered data layers may have limited significance when viewed separately, but once they are analyzed in series with the genome and transcriptome, they often lead to a more comprehensive understanding and also provide support for precision breeding (Zhang and Lin, 2025). 4 Integration Strategies and Analytical Methods for Multi-Omics Data 4.1 Data normalization and quality control procedures High-throughput platforms have a large amount of data, diverse types, and inconsistent data types. If you want to analyze these pieces of information from different "omics" together, the initial quality control and standardization steps are basically unavoidable. After receiving the sequencing read segments, the first steps are cleaning, comparison, and feature screening - these processes may sound highly technical, but in fact, they are all aimed at ensuring comparability among different samples. In data such as metagenomic and metabolomics, commonly used methods include accumulation and scaling, quantile normalization, etc., which can calibrate the deviations brought by batches (Ravelo et al., 2024). However, standardization is not as easy as simply aligning formats. Problems such as false positives, duplicate signals, and file structure differences are sometimes not sufficient to be automatically handled by software alone. When integrating across omics, if this step is not handled cleanly, no matter how fancy the subsequent analysis results are, they will not be reliable. 4.2 Multi-omics correlation and network modeling approaches When it comes to how to view data from different omics groups together, the set of network modeling comes in handy. Methods like WGCNA are not a recent trend; they have been widely used in multi-omics research for several years. It can cluster those gene, metabolite or microbial data into modules and then link them to specific phenotypes (such as milk production) (Zhang et al., 2023). The advantage of this kind of scale-free network analysis lies in its ability to clearly see the overall structure, not just focusing on a single gene. Integration frameworks like MOFA are also following suit. They not only look at common patterns but also pay attention to the sources of differences specific to each discipline (Wang et al., 2024). These models are more like "interpreting" the relationships between omics rather than simply piecing together a jigsaw puzzle. 4.3 Phenotype association analysis using machine learning and AI techniques The integration of AI into omics is actually no longer a novelty. Whether it is milk production, milk fat ratio or protein level, it is always somewhat difficult to predict these traits with traditional statistical methods. So, machine learning began to take the lead. Regression methods like ridge regression and LASSO, or ensemble models such
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