Programme(s) to which this project applies:
|☑ MPhil/PhD||☒ MRes[Med]||☒ URIS|
Cancer is a heterogeneous disease that evolves dynamically over time and in response to treatments. The adaptability of cancer presents a major therapeutic challenge. To address this, researchers have generated genomic data for >1.6 million cancer samples under numerous treatment conditions from diverse histopathological subtypes and disease stages. This wealth of data represents an incredible opportunity to uncover hidden information and derive critical insights with advanced data science techniques. However, these datasets have been underutilized because they are technically heterogeneous and difficult to integrate together without introducing artifacts and biases. Therefore, we propose to build a universal genomic map of cancers from this big heterogeneous genomic data by leveraging artificial intelligence algorithms and developing deep neural networks for representation learning. In order to integrate the genomic data effectively, we will use our extensive expertise in normalizing diverse datasets for constructing novel hierarchical Bayesian models of data harmonization. Indeed, our map will be a valuable resource for fertilizing the research on cancer genomics and for catalyzing the discovery of novel therapeutics.
We seek motivated candidates with interest in cancer genomics, Bayesian modelling, or deep learning. Ideal candidates will have strong programming or mathematical skills. Prior experience in computational biology is not necessary.
For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description. Interested candidates are advised to enclose with your email:
Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to firstname.lastname@example.org.
HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.
HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.