Multimodal AI for Multi-omics Data Integration

Due to advances in genomics, transcriptomics, imaging and other modalities, health datasets are becoming increasingly rich, capturing detailed information about an individual's genotype and phenotype. Data from different modalities may provide complementary information, as well as exhibit correlated and/or causal relationships. But how to best combine multi-omics data for holistic prediction? This PhD will explore novel machine and deep learning solutions that integrate multi-omics data, with application to drug discovery science. Using neurodegenerative diseases like ALS, Alzheimer's and Parkinson's as exemplar domains, you will research AI data modelling and fusion methods to recognise patterns across multiple modalities, addressing challenges such as handling several omics modalities, data imbalances and missing modalities/features. Expected outcomes include new general purpose AI tools widely applicable to multi-omics data along with application of these tools in the discovery of new targets and biomarkers.

The project will benefit from a number of emerging high-dimensional human datasets in neurodegenerative conditions that amalgamate transcriptomics, genetics, imaging and proteomic data and link them to disease phenotypes (e.g. AMP-AD, PPMI and Answer ALS). This presents an opportunity to explore the state-of-the-art multimodal integration approaches to define molecular underpinning on these conditions to define causal biology of the disease and ultimately leading to new ways of improving health in these debilitating conditions