Integrative Survival Analysis of Breast Cancer with Gene Expression and DNA Methylation Data.
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Abstract
Integrative multi-feature fusion analysis on biomedical data has gained much attention recently. In breast cancer, existing studies have demonstrated that combining genomic mRNA data and DNA methylation data can better stratify can-cer patients with distinct prognosis than using single signature. However, those ex-isting methods are simply combining these gene features in series and have ignored the correlations between separate omics dimensions over time.
In the present study, we propose an adaptive multi-task learning method, which combines the Cox loss task with the ordinal loss task, for survival prediction of breast cancer patients using multi-modal learning instead of performing survival analysis on each feature data set. First, we use local maximum quasi-clique merging (lmQCM) algorithm to reduce the mRNA and methylation feature dimensions and extract cluster eigengenes respectively. Then, we add an auxiliary ordinal loss to the original Cox model to improve the ability to optimize the learning process in training and regularization. The auxiliary loss helps to reduce the vanishing gradient problem for earlier layers and helps to decrease the loss of the primary task. Meanwhile, we use an adaptive weights ap-proach to multi-task learning which weighs multiple loss functions by considering the homoscedas-tic uncertainty of each task. Finally, we build an ordinal cox hazards model for survival analysis and use long short-term memory (LSTM) method to predict patients’ survival risk. We use the cross-validation method and the concordance index (C-index) for assessing the prediction effect. Strin-gent cross-verification testing processes for the benchmark data set and two additional datasets demonstrate that the developed approach is effective, achieving very competitive performance with existing approaches.
https://github.com/bhioswego/ML_ordCOX.
Supplementary data are available at Bioinformatics online.
© The Author(s) 2021. Published by Oxford University Press.