Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research - Liu, Suran; You, Yujie; Tong, Zhaoqi; Zhang, Le (Frontiers in Genetics, 2021)
It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear, and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman, and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behaviour, a proteomics time series with oscillating behaviour, and a metabolomics time series with flow behaviour. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness, and generalisability to predict the future state of a time series for multi-omics data.