Genomics and Applied Biology 2024, Vol.15, No.4, 172-181 http://bioscipublisher.com/index.php/gab 175 systems. Recent advances in this area include the development of integrative machine learning models that can handle heterogeneous data sources and complex biological interactions (Li et al., 2016; Nicora et al., 2020). Techniques such as network-based methods, matrix factorization, and deep neural networks are employed to fuse data from different omics layers, enabling the identification of biomarkers and the elucidation of disease mechanisms (Li et al., 2016; Mirza et al., 2019; Nicora et al., 2020). These integrative approaches are particularly valuable in oncology, where they support precision medicine by providing actionable insights for patient treatment and drug repurposing (Nicora et al., 2020). 3.4 Bayesian methods in genomic research Bayesian methods offer a powerful framework for genomic research by incorporating prior information and modeling measurements with various distributions. These methods are particularly useful for integrating multi-view data and addressing the challenges of data heterogeneity and missing values. Bayesian models can infer direct and indirect associations in heterogeneous networks, making them suitable for complex biological data integration (Li et al., 2016). Additionally, Bayesian approaches are employed in the analysis of single-cell genomics data, where they help in trajectory inference, cell type classification, and gene regulatory network inference (Raimundo et al., 2021). The flexibility and robustness of Bayesian methods make them a valuable tool in the ongoing efforts to understand the genetic underpinnings of complex traits and diseases (Ritchie et al., 2015). 4 Challenges in Biostatistical Applications in Genomics 4.1 Handling big data and computational complexity The rapid advancements in genomic technologies, particularly next-generation sequencing, have led to an explosion of genomic data. This data is not only vast in volume but also highly diverse, posing significant challenges in terms of computational complexity and data management. For instance, the large feature space of genome-wide data increases computational demands, making scalability a major issue. Novel approaches such as convolutional Wasserstein GANs (WGANs) and conditional RBMs (CRBMs) have been developed to address these challenges by generating high-quality artificial genomes while managing computational loads effectively (Yelmen et al., 2023). Additionally, the integration and manipulation of diverse genomic data and electronic health records (EHRs) require sophisticated Big Data analytics to uncover hidden patterns and clinically actionable insights (He et al., 2017). 4.2 Issues with data quality and standardization The quality and standardization of genomic data are critical for reliable analysis and interpretation. High-throughput technologies generate large pools of sensitive information that are often difficult to interpret due to inconsistencies and lack of standardization. This issue is compounded by the need for sustainable infrastructure and state-of-the-art tools for efficient data management (Umbach et al., 2019). Moreover, the success of genomic research hinges on the reproducibility and interpretability of results, which are often hampered by the lack of standardized bioinformatics pipelines (Davis-Turak et al., 2017). Ensuring data quality and standardization is essential for bridging the gap between genotype and phenotype and for the effective clinical application of genomic data. 4.3 Addressing population structure and genetic diversity Genomic data is inherently complex due to the diverse genetic backgrounds of different populations. This diversity poses challenges in accurately characterizing population structure and linkage disequilibrium, which are crucial for understanding genetic variations and their implications. Generative models like GANs and RBMs have shown promise in preserving complex characteristics of real genomes, such as population structure and selection signals, but there is still room for improvement in terms of genome quality and privacy preservation (Yelmen et al., 2023). Additionally, the state of population genetics theory needs substantial improvement to effectively handle the forensic use of genome-wide data, highlighting the need for better biostatistical modeling (Amorim and Pinto, 2018).
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