Integrated multi-omics analysis of ovarian cancer using variational autoencoders
Abstract Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In rec...
Saved in:
Main Authors: | Muta Tah Hira, M. A. Razzaque, Claudio Angione, James Scrivens, Saladin Sawan, Mosharraf Sarkar |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/3df6c2d9b23b40b1a3a6c34eb8bb4cdb |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Author Correction: Integrated multi‑omics analysis of ovarian cancer using variational autoencoders
by: Muta Tah Hira, et al.
Published: (2021) -
Explore Protein Conformational Space With Variational Autoencoder
by: Hao Tian, et al.
Published: (2021) -
Conditional Variational Autoencoder for Learned Image Reconstruction
by: Chen Zhang, et al.
Published: (2021) -
Adversarial Attention-Based Variational Graph Autoencoder
by: Ziqiang Weng, et al.
Published: (2020) -
Multi-omic Characterization of Intraspecies Variation in Laboratory and Natural Environments
by: Megan G. Behringer
Published: (2021)