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[Malaysia] - Predictive modelling of chemoresistance in cancer cell lines using machine learning

Primary supervisor

Ong Huey Fang

Following the success of the Human Genome Project, the entire scientific community witnessed a large data explosion in genomics, which was also aided by advances in molecular biology technologies such as next-generation sequencing. These high-throughput technologies enable comprehensive molecular profiling of cancer cell lines, including gene expression. Regardless of the use of gene-based assays, they provide abundant genomic information for identifying participating genes (biomarkers) that contribute to the chemoresistance process in cancer cells.

Predictive modelling using machine learning (ML) techniques allows the generation of predictive biomarkers for chemotherapeutic response and targets for new chemotherapeutics agents. However, traditional ML techniques have some computational challenges, including (1) curse of dimensionality, (2) class imbalance, (3) heterogeneity, (4) lack of context, and (5) robust and interpretable outcomes. Therefore, a better strategy could be proposed to complement ML techniques by combining heterogeneous and large-scale datasets based on a pan-genome approach.


Student cohort

Double Semester


This research aims to build predictive models for chemoresistance in cancer cell lines through machine-learning approaches. We will focus on specific cancer cell lines and leverage multiple types of datasets such as gene expression profiles, drug sensitivity, and functional annotations. Improved algorithms with advantages in integration and prediction capabilities will be proposed.

Required knowledge

Has skills in programming and machine learning.