NDLI: A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction

Content Provider	Springer Nature : BioMed Central
Author	Tan, Kaiwen Huang, Weixian Hu, Jinlong Dong, Shoubin
Abstract	Background With the rapid development of sequencing technologies, collecting diverse types of cancer omics data become more cost-effective. Many computational methods attempted to represent and fuse multiple omics into a comprehensive view of cancer. However, different types of omics are related and heterogeneous. Most of the existing methods do not consider the difference between omics, so the biological knowledge of individual omics may not be fully excavated. And for a given task (e.g. predicting overall survival), these methods prefer to use sample similarity or domain knowledge to learn a more reasonable representation of omics, but it’s not enough. Methods For the purpose of learning more useful representation for individual omics and fusing them to improve the prediction ability, we proposed an autoencoder-based method named MOSAE (Multi-omics Supervised Autoencoder). In our method, a specific autoencoder were designed for each omics according to their size of dimension to generate omics-specific representations. Then, a supervised autoencoder was constructed based on specific autoencoder by using labels to enforce each specific autoencoder to learn both omics-specific and task-specific representations. Finally, representations of different omics that generate from supervised autoencoders were fused in a traditional but powerful way, and the fused representation was used for subsequent predictive tasks. Results We applied our method over TCGA Pan-Cancer dataset to predict four different clinical outcome endpoints (OS, PFI, DFI, and DSS). Compared with traditional and state-of-the-art methods, MOSAE achieved better predictive performance. We also tested the effects of each improvement, which all have a positive effect on predictive performance. Conclusions Predicting clinical outcome endpoints are very important for precision medicine and personalized medicine. And multi-omics fusion is an effective way to solve this problem. MOSAE is a powerful multi-omics fusion method, which can generate both omics-specific and task-specific representation for given endpoint predictive tasks and improve the predictive performance.
Related Links	https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-020-1114-3.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14726947
DOI	10.1186/s12911-020-1114-3
Journal	BMC Medical Informatics and Decision Making
Issue Number	3
Volume Number	20
Language	English
Publisher	BioMed Central
Publisher Date	2020-07-09
Access Restriction	Open
Subject Keyword	Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Multic-omics Autoencoder Fusion Representation Pan-Cancer Endpoints
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications Health Policy
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.9/2023

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