  High-dimensional omics data contains intrinsic biomedical information that is
crucial for personalised medicine. Nevertheless, it is challenging to capture
them from the genome-wide data due to the large number of molecular features
and small number of available samples, which is also called "the curse of
dimensionality" in machine learning. To tackle this problem and pave the way
for machine learning aided precision medicine, we proposed a unified multi-task
deep learning framework called OmiEmbed to capture a holistic and relatively
precise profile of phenotype from high-dimensional omics data. The deep
embedding module of OmiEmbed learnt an omics embedding that mapped multiple
omics data types into a latent space with lower dimensionality. Based on the
new representation of multi-omics data, different downstream networks of
OmiEmbed were trained together with the multi-task strategy to predict the
comprehensive phenotype profile of each sample. We trained the model on two
publicly available omics datasets to evaluate the performance of OmiEmbed. The
OmiEmbed model achieved promising results for multiple downstream tasks
including dimensionality reduction, tumour type classification, multi-omics
integration, demographic and clinical feature reconstruction, and survival
prediction. Instead of training and applying different downstream networks
separately, the multi-task strategy combined them together and conducted
multiple tasks simultaneously and efficiently. The model achieved better
performance with the multi-task strategy comparing to training them
individually. OmiEmbed is a powerful tool to accurately capture comprehensive
phenotypic information from high-dimensional omics data and has a great
potential to facilitate more accurate and personalised clinical decision
making.
