Recent breakthroughs in artificial intelligence, particularly in deep learning, have revolutionized our ability to analyze and interpret biological data, including DNA, RNA, and protein sequences. These advances have led to significant progress in critical fields such as medicine, agriculture, and biotechnology.
In this talk, I will present my research on applying deep learning, especially natural language processing (NLP) techniques, to address central challenges in bioinformatics. I will briefly discuss parallels between natural language and biological sequences, along with strategies for adapting NLP methods to account for the key differences between them. I will begin by presenting how the choice of tokenizer can improve model performance while significantly reducing memory usage, a common challenge in deep learning.
Next, I will discuss our deep learning models for computing multiple sequence alignments, demonstrating that transformer architectures can produce accurate results. I will then present a hybrid model capable of generating ancestral sequences without requiring a specific sequence alignment or phylogenetic tree as input. Our results show that this approach performs competitively and, in some cases, surpasses traditional methods.
Finally, I will introduce a large language model trained to predict protein function, which outperforms traditional search algorithms, particularly for proteins with low similarity to known sequences. Overall, my research highlights how deep learning and NLP offer powerful new solutions to long-standing challenges in bioinformatics.