Deciphering genomic codes using advanced NLP techniques: a scoping review

One limitation of this study is the constrained scope of the literature review sources, which primarily includes human genome data and only limited exploration of bacterial, viral, and other non-human DNA. Moreover, the study predominantly focuses on cancer when it comes to disease analysis, giving relatively less attention to other disease domains that involve complex DNA interactions, such as neurodegenerative diseases, autoimmune diseases, and genetic disorders. These areas also offer rich opportunities for genomic research and could benefit from applying NLP techniques.

… Including clinical data can enhance model predictions by grounding them in real-world patient information, thereby improving clinical relevance and enabling personalized insights. This comprehensive approach can provide a more comprehensive understanding of the regulatory mechanisms governing gene expression and the interplay between different molecular layers. It can also enable models to generate and validate more accurate and biologically meaningful predictions, thereby increasing physicians’ confidence in NLP-generated results and promoting the widespread application of NLP-based models.