My scientific career is driven by the fundamental challenge of uncovering the link between genes and their functions, aiming to fill gaps in biological knowledge using computational algorithms tailored to big data. I have developed computational tools for constructing and analyzing regulatory networks in specific biological pathways. My primary goal is to enhance the integration of computational biology and data science into clinical research, particularly in tackling Mycobacterium tuberculosis (MTB) drug resistance.
Since transitioning to clinical sciences in September 2019, I have gradually expanded my research interests in multi-omics, which integrates information from orthogonal technologies (e.g., RNA/DNA NGS, proteomics, and structural biology) and different molecular layers (e.g., metabolites, proteins, and genes). Additionally, I have developed expertise in artificial intelligence, including the advanced use of large language models (LLMs) to integrate and analyze complex biological datasets, facilitating novel hypothesis generation and accelerating research workflows.
During my career, I have developed several computational user-friendly tools aimed at utilizing omics data sets for biological discoveries.
An example of these tools includes: