Computational Molecular Biology 2024, Vol.14, No.2, 76-83 http://bioscipublisher.com/index.php/cmb 80 6 Case Studies 6.1 Successful applications in genomics Deep learning has significantly impacted genomics, providing higher accuracies in specific tasks compared to traditional methodologies. For instance, deep learning models have been successfully applied to regulatory genomics, variant calling, and pathogenicity scoring, demonstrating their ability to identify complex patterns in large genomic datasets (Zou et al., 2018). Additionally, the integration of deep learning with high-throughput sequencing technologies has enabled researchers to analyze vast amounts of genomics data, facilitating precision medicine through multi-scale and multimodal data analysis (Figure 1) (Koumakis, 2020). These advancements underscore the potential of deep learning to accelerate changes in genomics research, particularly in the analysis and interpretation of big data (Min et al., 2016). Figure 1 Multi level and multi scale -omics models (Adopted from Koumakis, 2020) Image caption: Deep learning has the ability to deal with multimodal data effectively and genomics offers extremely heterogeneous data. The notion of precision medicine is based on the multimodal data analysis (Adopted from Koumakis, 2020) 6.2 Deep learning in drug development The application of deep learning in drug development has shown promising results, particularly in in silico drug discovery and development. Graph neural networks (GNNs), a type of deep learning model, have been employed to predict protein-protein interactions and protein functions, which are crucial for identifying potential drug targets (Muzio et al., 2020). Furthermore, deep learning models can predict how small molecules will modulate the activity of therapeutically relevant proteins, aiding in the identification of new drug candidates (Wainberg et al., 2018). These models have also been integrated with established bioinformatics frameworks to enhance the study of complex biological systems, thereby improving the efficiency and accuracy of drug development processes (Figure 2) (Auslander et al., 2021). 6.3 Predictive modeling in disease research Deep learning has also made significant strides in disease research through predictive modeling. For example, deep learning algorithms have been used to predict disease progression and treatment outcomes by analyzing biomedical big data (Min et al., 2016). In the context of disease diagnosis, deep learning models have been applied to gene regulatory networks to predict gene interactions and automatically diagnose diseases from data (Muzio et al., 2020). Additionally, these models have been utilized to analyze medical images and physiological data, such as electroencephalography, to understand human health and disease better (Cao et al., 2018). The ability of deep learning to integrate vast datasets and learn complex relationships has made it a valuable tool in predictive modeling for disease research (Wainberg et al., 2018).
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