Abstract
High-dimensional omics data contain 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 named OmiEmbed to capture biomedical information from
high-dimensional omics data with the deep embedding and downstream task
modules. The deep embedding module 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 task modules were trained simultaneously and
efficiently with the multi-task strategy to predict the comprehensive
phenotype profile of each sample. OmiEmbed supports multiple tasks for
omics data including dimensionality reduction, tumour type
classification, multi-omics integration, demographic and clinical
feature reconstruction, and survival prediction. The framework
outperformed other methods on all three types of downstream tasks and
achieved better performance with the multi-task strategy compared to
training them individually. OmiEmbed is a powerful and unified framework
that can be widely adapted to various applications of high-dimensional
omics data and has great potential to facilitate more accurate and
personalised clinical decision making.
Original language | English |
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Article number | 3047 |
Number of pages | 22 |
Journal | Cancers |
Volume | 13 |
Issue number | 12 |
DOIs | |
Publication status | Published - 18 Jun 2021 |
Scopus Subject Areas
- Oncology
- Cancer Research
User-Defined Keywords
- Cancer classification
- Deep learning
- Multi-omics data
- Multi-task learning
- Survival prediction