Abstract:
Generative genomics generally refers to a study area that undertakes computational model devel opment and algorithms capable of developing synthetic genomic data. This report explores the application of transformer-based architectures for generating synthetic genomic data. The litera ture review provides an overall view of different architectures based on the transformer explored through the term. It also discusses some challenges during implementation and their solutions with innovation presented for the first time. It also covers the possible applications of generative genomics and the area where the scope for future research in the evolving field lies. Retrieval-Augmented Generation (RAG) is a powerful technique in the field of Natural Language Processing (NLP) that combines the benefits of both retrieval-based and generative models. RAG models integrate a re trieval step into the generative process, allowing the model to access external knowledge stored in large-scale databases or corpora during the generation of text.
