View-Aware Collaborative Learning for Survival Prediction and Subgroup Identification

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Original languageEnglish
Pages (from-to)307-317
Journal / PublicationIEEE Transactions on Biomedical Engineering
Issue number1
Online published12 Jul 2022
Publication statusPublished - Jan 2023


Advances of high throughput experimental methods have led to the availability of more diverse omic datasets in clinical analysis applications. Different types of omic data reveal different cellular aspects and contribute to the understanding of disease progression from these aspects. While survival prediction and subgroup identification are two important research problems in clinical analysis, their performance can be further boosted by taking advantages of multiple omics data through multi-view learning. However, these two tasks are generally studied separately, and the possibility that they could reinforce each other by collaborative learning has not been adequately considered. In light of this, we propose a View-aware Collaborative Learning (VaCoL) method to jointly boost the performance of survival prediction and subgroup identification by integration of multiple omics data. Specifically, survival analysis and affinity learning, which respectively perform survival prediction and subgroup identification, are integrated into a unified optimization framework to learn the two tasks in a collaborative way. In addition, by considering the diversity of different types of data, we make use of the log-rank test statistic to evaluate the importance of different views. As a result, the proposed approach can adaptively learn the optimal weight for each view during training. Empirical results on several real datasets show that our method is able to significantly improve the performance of survival prediction and subgroup identification. A detailed model analysis study is also provided to show the effectiveness of the proposed collaborative learning and view-weight learning approaches.

Research Area(s)

  • Analytical models, Clinical diagnosis, Collaboration, Collaborative learning, Collaborative work, Diseases, Genomics, Multi-view learning, Task analysis, View-weight learning