DeepOmix A Multi-Omics Scalable and Interpretable Deep Learning Framework and Application in Cancer Survival Analysis
Introduction
Integrative analysis of multi-omics data can elucidate valuable insights into complex molecular mechanisms for various diseases. However, due to their different modalities and high dimension, utilizing and integrating different types of omics data suffers from great challenges. There is an urgent need to develop a powerful method to improve survival prediction and detect functional gene modules from multi-omics data. To deal with these problems, we present DeepOmix (a scalable and interpretable multi-Omics Deep learning framework and application in cancer survival analysis), a flexible, scalable, and interpretable method for extracting relationships between the clinical survival time and multi-omics data based on a deep learning framework. DeepOmix enables the non-linear combination of variables from different omics datasets and incorporates prior biological information defined by users (such as signaling pathways and tissue networks). Benchmark experiments demonstrate that DeepOmix outperforms the other five cutting-edge prediction methods. Besides, Lower Grade Glioma (LGG) is taken as the case study to perform the prognosis prediction and illustrate the functional module nodes which are associated with the prognostic result in the prediction model.
Publications
DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis
Cited by 0 (Google Schoolar as of September 16, 2021)
Credits
- Dechao Bu budechao@ict.ac.cn Contributor
Cloud Platform, Beijing Zhong Ke Jing Yun, China
Community Ratings
Usability | Efficiency | Reliability | Rated By |
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Accession | BT007270 |
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Tool Type | Application |
Category | Multi-omic data integration |
Platforms | Linux/Unix |
Technologies | Python3 |
User Interface | Terminal Command Line |
Input Data | VCF |
Download Count | 0 |
Submitted By | Dechao Bu |
国家重点研发计划课题”精准医学大数据分析应用方法体系"[2018YFC0910400, 2018YFC0910401]