Integrating multi-omics data through deep learning for accurate cancer prognosis prediction

Chai, Hua; Zhou, Xiang; Zhang, Zhongyue; Rao, Jiahua; Zhao, Huiying; Yang, Yuedong

doi:10.1016/j.compbiomed.2021.104481

Cited by 85 publications

(55 citation statements)

References 36 publications

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“…For the same clinical task, a similar workflow has been adapted by researchers, but applying denoising AEs (DAEs) [ 91 ] instead [ 35 , 36 ]. By adding noise to the input \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$x$\end{document} , but not to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$x$\end{document} in the reconstruction loss (Equation 1 ), the DAE has to learn a reconstruction and also remove noise to approximate the uncorrupted vector x .…”

Section: Early Fusionmentioning

confidence: 99%

“…The applications of early nonlinear fusion methods reviewed here have shown that these methods can outperform shallow methods on prediction tasks (e.g. [ 35 , 36 ]). This demonstrates that DL methods are viable alternatives to traditional methods even when sample sizes are comparatively low, because there were as low as 96 patients in the applications reviewed above [ 28 ].…”

Section: Early Fusionmentioning

confidence: 99%

“…To avoid the assumption that all sub-models hold equally relevant information to predict the target, other aggregation methods can be employed. Questioning this assumption is relevant as many methods have shown unequal contribution of different modalities to the predictive performance (see for example [ 36 , 42 ]). Wang et al .…”

Section: Late Fusionmentioning

confidence: 99%

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Multimodal deep learning for biomedical data fusion: a review

Stahlschmidt

Ulfenborg

Synnergren

2022

Briefings in Bioinformatics

199

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Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships. Therefore, we review the current state-of-the-art of such methods and propose a detailed taxonomy that facilitates more informed choices of fusion strategies for biomedical applications, as well as research on novel methods. By doing so, we find that deep fusion strategies often outperform unimodal and shallow approaches. Additionally, the proposed subcategories of fusion strategies show different advantages and drawbacks. The review of current methods has shown that, especially for intermediate fusion strategies, joint representation learning is the preferred approach as it effectively models the complex interactions of different levels of biological organization. Finally, we note that gradual fusion, based on prior biological knowledge or on search strategies, is a promising future research path. Similarly, utilizing transfer learning might overcome sample size limitations of multimodal data sets. As these data sets become increasingly available, multimodal DL approaches present the opportunity to train holistic models that can learn the complex regulatory dynamics behind health and disease.

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Section: Early Fusionmentioning

confidence: 99%

Section: Early Fusionmentioning

confidence: 99%

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Multimodal deep learning for biomedical data fusion: a review

Stahlschmidt

Ulfenborg

Synnergren

2022

Briefings in Bioinformatics

199

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“…In this framework, data integration has been an active field of research for ML and DL techniques applied to omics data, especially cancer genomics [180] , [181] (see Section 4.3 for a more detailed discussion on data integration). In particular, the introduction of autoencoders, such as denoising autoencoders, has allowed robust representations of heterogeneous data to be provided, and extraction of highly representative and predictive features to be more easily performed [182] , [183] , [184] . Indeed, AI applications to cancer genomics can provide useful information for a rapid growth of precision medicine and for disease prevention and monitoring.…”

Section: Ai Applications In Functional Genomicsmentioning

confidence: 99%

AI applications in functional genomics

Caudai

Galizia

Geraci

et al. 2021

Computational and Structural Biotechnology Journal

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“…Katzman proposed a deep survival method (Deep_surv) for the estimation of cancer outcomes with a three-hidden-layer deep neural network (10). Yet, these methods are limited due to cancer data from small sample sizes (11,12). One common strategy to solve this problem is transfer learning, where models are pre-trained based on similar cancer types and then fine-tuned on the target cancer type (13,14).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction

et al. 2021

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BackgroundPredicting hepatocellular carcinoma (HCC) prognosis is important for treatment selection, and it is increasingly interesting to predict prognosis through gene expression data. Currently, the prognosis remains of low accuracy due to the high dimension but small sample size of liver cancer omics data. In previous studies, a transfer learning strategy has been developed by pre-training models on similar cancer types and then fine-tuning the pre-trained models on the target dataset. However, transfer learning has limited performance since other cancer types are similar at different levels, and it is not trivial to balance the relations with different cancer types.MethodsHere, we propose an adaptive transfer-learning-based deep Cox neural network (ATRCN), where cancers are represented by 12 phenotype and 10 genotype features, and suitable cancers were adaptively selected for model pre-training. In this way, the pre-trained model can learn valuable prior knowledge from other cancer types while reducing the biases.ResultsATRCN chose pancreatic and stomach adenocarcinomas as the pre-training cancers, and the experiments indicated that our method improved the C-index of 3.8% by comparing with traditional transfer learning methods. The independent tests on three additional HCC datasets proved the robustness of our model. Based on the divided risk subgroups, we identified 10 HCC prognostic markers, including one new prognostic marker, TTC36. Further wet experiments indicated that TTC36 is associated with the progression of liver cancer cells.ConclusionThese results proved that our proposed deep-learning-based method for HCC prognosis prediction is robust, accurate, and biologically meaningful.

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Integrating multi-omics data through deep learning for accurate cancer prognosis prediction

Cited by 85 publications

References 36 publications

Multimodal deep learning for biomedical data fusion: a review

Multimodal deep learning for biomedical data fusion: a review

AI applications in functional genomics

An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction

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