Statistical methods for data generated by biotechnologies

Biotechnological advancements are opening new doors in clinical and biological sciences by enabling in-depth exploration of disease markers and pathways that were previously unimaginable. However, these advancements bring data challenges due to the complexity and vast amount of information involved. Traditional analysis methods often fall short in capturing the dynamic nature of biological processes, usually simplifying gene expression into static views. We propose a new statistical approach to identify key genes affected by a condition, which could lead to better treatments and medications by revealing the molecular mechanisms behind diseases and how they respond to treatment. Our method will improve an existing robust gene set analysis technique called the linear combination test by including gene regulatory networks. These networks are essential for controlling basic biological processes, and understanding them is crucial for grasping the complex and dynamic mechanisms of living organisms. We will validate this enhanced method using publicly available real-world data and develop an easy-to-use R-based software package. This bioinformatics tool could speed up drug discovery and development, moving us closer to more personalized and targeted treatment strategies. Our method will also help other research groups use biotechnological data to tackle innovative biomedical research questions.

Faculty Supervisor:

Irina Dinu;Morteza Hajihosseini

Student:

Partner:

Applied Pharmaceutical Innovation

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services; Retail trade

University:

University of Alberta

Program: