Skip to main content

[Bioinformatics Project] Applying machine learning approaches to predict anti-cancer drug efficacy in patients

Primary supervisor



  • Dr Lan Nguyen
  • Dr Sungyoung Shin

Despite enormous progress in research, cancer remains a devastating disease worldwide. Since generally not all patients will respond to a specific therapy, a great challenge in cancer treatment is the ability to predict which patients would benefit (or not) to a therapy of choice. This helps improve treatment efficacy and minimise unnecessary sufferings by non-responders. There is thus a pressing need to identify robust biomarkers (i.e. genes/proteins) that can accurately predict the right patients for the right drugs. With the increasing availability of molecular and drug-response data, machine learning approaches provide a powerful tool for this task. This project will utilise key data science techniques including data processing, integration, analysis and visualisation; and then use these data to develop useful machine learning models to identify optimal biomarkers for different cancer types. This work will build upon a Support Vector Machine-based pipeline in the Nguyen lab.

Students will have a unique opportunity to work with real-life biomedical data and contribute towards solving a critical challenge in cancer research in Dr Lan Nguyen’s lab. They will be able to develop their skills in a highly interdisciplinary research group with expertise in both computational & biomedical fields. There are also potential opportunities to continue to PhD studies.

Note on project - Preferably 2 semesters, but the project is currently designed for 1 semester.


For more information, contact the primary supervisor Dr Lan Nguyen <>

Student cohort

Single Semester
Double Semester

Required knowledge

  • Candidate students should have taken the Introduction to Bioinformatics unit.
  • Experience in Python, Java, or R is essential.
  • Experience with Machine Learning is preferable.