# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 3 Artificial Intelligence in Genetics and Genomics
### AI in Genetics and Computational Genomics
In the context of genome-wide association studies (GWAS), which aim to identify associations between genetic variants and traits or diseases, AI techniques have been invaluable in managing the immense computational burden associated with these studies. GWAS typically involve scanning millions of genetic variants across large populations to identify those associated with specific phenotypes. Traditional GWAS methods rely on linear models and statistical thresholds to detect significant associations, but these approaches may fail to capture complex interactions between genetic variants (epistasis) or between genes and environmental factors. AI, particularlyin the form of deep learning and neural networks, can model these nonlinear interactions and capture subtle genetic effects that might be missed by conventional methods [50]. For instance, AI algorithms can integrate genomic data with other layers of biological data, such as transcriptomics, epigenomics, and proteomics, to provide a more holistic view of the genetic architecture underlying complex diseases like cancer, diabetes, and neurodegenerative disorders [42; 51]. One of the most promising areas of AI application in genomics is drug discovery and development [52]. Genomic data play a critical role in identifying drug targets and understanding the genetic basis of diseases, and AI can accelerate this process by analyzing large-scale genomic datasets to identify patterns that human researchers might miss [42]. For example, AI models can be trained on genomic data from patients with a particular disease to identify common genetic variants or gene expression profiles that could be targeted by new drugs, or can be utilized to predict the pathogenicity of variants, map an individual's sequence to genome references, and identify any clinically significant mutations (Figure 1). In addition to target identification, AI can also be used to predict how patients with different genetic backgrounds might respond to certain drugs, thus paving the way for more personalized treatments. This approach, often referred to as pharmacogenomics, is particularly important for diseases like cancer, where genetic mutations can influence drug efficacy and resistance. AI-driven insights from genomic data can help researchers develop targeted therapies that are more likely to be effective for specific patient populations, thereby reducing the time and cost associated with traditional drug discovery processes [51]. In addition to these specific applications, AI has become an integral part of data integration and interpretation in genomics. The wealth of genomic data generated by sequencing technologies is often accompanied by other types of omics data, such as transcriptomics (gene expression), proteomics (protein levels), and epigenomics (DNA methylation patterns) [53]. Integrating these diverse datasets is essential for gaining a comprehensive understanding of biological processes, but it is also extremely challenging due to the sheer volume and heterogeneity of the data. AI models, particularly those using unsupervised learning techniques, can help by identifying hidden patterns and relationships within the data that might not be apparent through traditional analytical methods. For example, AI-driven approaches can cluster similar samples based on multi-omic data, revealing subtypes of diseases that were previously unrecognized. This capability is especially valuable in cancer research, where tumors are often genetically heterogeneous, and identifying subtypes can lead to more effective, personalized treatment strategies [54]. Figure 1: Example of the use of artificial intelligence to predict the pathogenicity of variants [55].