Our team has developed a state-of-the-art model that identifies novel biomarkers to predict the response of drugs after short-term treatment for cancer. This research aims to investigate the association between treatment response and genomics modules and uncover key biological processes and pathways in cancer.
Through advanced computational methods and machine learning algorithms, we've created predictive models that can identify potential treatment outcomes based on genetic information, helping clinicians make more informed decisions about patient care.
We specialize in integrating multiple types of -omics data (genomics, transcriptomics, proteomics, etc.) to gain a more comprehensive understanding of complex biological systems. By combining these diverse data types, we can identify correlations and patterns that would not be apparent when analyzing each data type in isolation.
Our multi-omics approach has led to breakthroughs in understanding disease mechanisms, identifying novel therapeutic targets, and developing personalized treatment strategies based on individual genetic profiles.
Our ongoing research projects include: