Gene Editing using Transformer Architecture

In order to enable efficient comparison among genetic sequences, specialized neural networks based on Transformer architecture, called TASAG (Transformer for Semantic Analysis of Genetic Sequences), were developed. TASAG networks assess both similarities and differences between genetic sequences. Users input chromosome files containing target genes, and TASAG compares these sequences to detect variations. The networks benefit from pre-training on annotated datasets, which highlight functional codons and their associated proteins, allowing TASAG to identify key regions for gene expression. Gene annotation further enhances this process.

Once TASAG detects a deviation from a reference sequence (e.g., the H-Bot sequence), it facilitates on-screen gene editing, enabling targeted mutations or the insertion of desired genes. Implementation requires Python and deep learning frameworks like TensorFlow or PyTorch, with optional use of Biopython for genetic sequence handling. Read more ...

Garg, R. , Vyas, A. , Khan, A. , Tariq, M. (2024), 'Codes beyond Bits and Bytes: A Blueprint for Artificial Life', World Academy of Science, Engineering and Technology, Open Science Index 213, International Journal of Biotechnology and Bioengineering, 18(9), 114 - 126.