Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

  • Research theme/s:

    Healthcare

  • Principal investigator (PI):

    Dr. Mohammad Yaqub

  • Researcher/s:

    Nil

  • Funding:

    MBZUAI

  • Department:

    Machine learning

  • Co-PI:

    Dr. Karthik Nandakumar

  • Student/s

    Sayed Hashim

  • Collaborators / partners:

    Nil

According to WHO, cancer accounted for around 10 million deaths in 2020, or about one in six deaths. Many of the cancers can be cured with early diagnosis and effective treatment. In our project, we use self-supervised learning to extract features from unlabelled data to perform cancer type classification. We work on multi-omics data obtained from Next Generation Sequencing for cancer diagnosis. Our approach works well even with the limited amount of labelled data.