Our research
Bioinformatics is a multidisciplinary field that combines biology, computer science, mathematics, and statistics to analyze and interpret biological data, particularly data from genomics (DNA-level biology), transcriptomics (RNA-level) and proteomics (protein-level), and other -omics technologies. For genomics, much of our work involves analysis of genome-wide association studies (GWAS), aiming to (i) identify genetic variants associated with diseases or traits and (ii) learn the genetic architecture of diseases/traits in terms of their heritability and genetic correlations. By analyzing genetic data from large populations, GWAS can pinpoint genetic loci linked to diseases, providing insights into their underlying mechanisms. This approach has revolutionized medical research by uncovering genetic factors influencing complex diseases such as diabetes, cancer, and cardiovascular disorders. Furthermore, GWAS findings facilitate the development of personalized medicine, enabling tailored interventions and treatments based on an individual's genetic makeup, ultimately leading to improved patient outcomes and healthcare strategies.
Analysis of transcriptomics or gene expression data involves studying the patterns and levels of gene activity in cells or tissues. These data can serve as valuable markers for instance for diagnosis and therapy response. Much of our recent works is in the latter problem. Firstly, by comparing gene expression profiles between treatment-responsive and non-responsive individuals, researchers can identify signature gene sets associated with positive outcomes. These signatures can be used to predict the response to a particular therapy, aiding in treatment selection for individual patients. Gene expression biomarkers can help the development of novel therapeutics by identifying potential targets or pathways involved in disease progression or treatment response. Overall, the analysis of gene expression data offers a powerful tool for personalized medicine, enabling the identification of predictive markers and the optimization of treatment strategies to improve patient outcomes.