Multiple Sequence Alignment in DNA Using Recurrent Neural Networks

In the realm of bioinformatics, this study addresses the challenge of multiple sequence alignment (MSA) in DNA, where conventional methods often encounter limitations in handling large-scale genomic data efficiently. To enhance alignment precision and computational efficiency, we propose the application of Long Short-Term Memory (LSTM) networks, a specialized type of Recurrent Neural Network (RNN). Trained to capture the intricate sequential dependencies inherent in DNA sequences, our LSTM-based approach is compared against traditional methods, showcasing promising improvements in alignment accuracy and computational efficiency. These initial results highlight the potential of LSTM networks to advance DNA sequence alignment, offering a novel solution to the complexities posed by diverse and evolving genetic information.