Gene Sequencing with Artificial Intelligence and Nuclear Magnetic Resonance (NMR) Spectroscopy

Artificial intelligence (AI) can generate gene sequences using DNA samples from organisms. This is achieved through Artificial Neural Networks (ANNs), particularly Convolutional Neural Networks (CNNs), which excel in image classification tasks. Specifically, a classifier CNN can be trained on a dataset composed of NMR spectra of nitrogenous bases.

To initiate this process, the DNA sample undergoes NMR spectroscopy to produce molecular structures. The classifier CNN is then trained using data representing the molecular structures of the four nitrogenous bases: Adenine, Guanine, Thymine, and Cytosine. During training, the CNN identifies these molecular structures within the sequence and labels them as 'A', 'G', 'T', or 'C' accordingly.

Upon receiving the NMR-derived molecular structures of these bases as inputs sequentially, the model can classify them into their respective genetic letters ('A', 'G', 'T', 'C'), thereby generating the gene sequence. 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.