Statistical and computational methods for analyzing omics data and predicting drug responses
Recent advancements in high-throughput sequencing technologies have enabled researchers to harness valuable omics data, paving the way for precision medicine. This approach aims to enhance diagnosis and treatment by tailoring therapies to individual patients, moving away from traditional, subjective methods. However, analyzing omics data for effective treatment personalization remains challenging due to disease variability and data complexity.
In a new thesis from Karolinska Institutet, Quang Thinh Trac at the Department of Medical Epidemiology and Biostatistics, presents innovative statistical and computational methodologies for multi-omics data analysis and drug response prediction, focusing on acute myeloid leukemia (AML) and amyotrophic lateral sclerosis (ALS).
What are the most important results in your thesis?
“We have developed novel statistical and computational methods to analyze omics data and responses of drugs. The results demonstrate that these methods perform well against existing methodologies. We hope that our work will advance omics data analysis and drug response prediction, aid researchers in uncovering biological insights, and contribute towards personalized treatment strategies for complex diseases.”
Why did you become interested in this topic?
“I have always been interested in working with data since I was an undergraduate student. During my final year at university, I completed my thesis on a bioinformatics topic, which made me realize that working with biological data was particularly fascinating. Additionally, coming from a Computer Science background, I have always been excited to see my computational work being useful and applied by others. This PhD topic perfectly aligns with both my data and computational interests, allowing me to explore data while also contributing valuable work to advance research in the field.”
What do you think should be done in future research?
“From the perspective of my PhD topic, I believe precision and personalized medicine will be a promising direction for the future of healthcare and research. This shift is already happening, as precision testing, diagnosis, and treatment are gradually being translated into clinical settings. However, for precision and personalized medicine to be successful, advancements in other fields, such as technology and a deeper understanding of biology, are also necessary. For example, the recent application of AI in medicine, such as drug design and AI-assisted pathology, appears very promising. Overall, it will be exciting to witness the evolution of healthcare and research in the near future.”
Doctoral thesis
“Statistical and computational methodologies for omics data analyses and drug response prediction.”
Quang Thinh Trac. Stockholm: Karolinska Institutet (2024), ISBN: 978-91-8017-738-2