skip to main content
Guest
My Research
My Account
Sign out
Sign in
This feature requires javascript
Library Search
Find Databases
Browse Search
E-Journals A-Z
E-Books A-Z
Citation Linker
Help
Language:
English
Vietnamese
This feature required javascript
This feature requires javascript
Primo Search
All Library Resources
All
Course Materials
Course Materials
Search For:
Clear Search Box
Search in:
All Library Resources
Or hit Enter to replace search target
Or select another collection:
Search in:
All Library Resources
Search in:
Print Resources
Search in:
Digital Resources
Search in:
Online E-Resources
Advanced Search
Browse Search
This feature requires javascript
Search Limited to:
Search Limited to:
Resource type
criteria input
All items
Books
Articles
Images
Audio Visual
Maps
Graduate theses
Show Results with:
criteria input
that contain my query words
with my exact phrase
starts with
Show Results with:
Search type Index
criteria input
anywhere in the record
in the title
as author/creator
in subject
Full Text
ISBN
ISSN
TOC
Keyword
Field
Show Results with:
in the title
Show Results with:
anywhere in the record
in the title
as author/creator
in subject
Full Text
ISBN
ISSN
TOC
Keyword
Field
This feature requires javascript
Progressively Helical Multi-Omics Data Fusion GCN and Its Application in Lung Adenocarcinoma
Access, IEEE, 2023, Vol.11, p.73568-73582
2013 IEEE ;DOI: 10.1109/ACCESS.2023.3296474
Full text available
Citations
Cited by
View Online
Details
Recommendations
Reviews
Times Cited
External Links
This feature requires javascript
Actions
Add to My Research
Remove from My Research
E-mail
Print
Permalink
Citation
EasyBib
EndNote
RefWorks
Delicious
Export RIS
Export BibTeX
This feature requires javascript
Title:
Progressively Helical Multi-Omics Data Fusion GCN and Its Application in Lung Adenocarcinoma
Author:
Zhu, Junxuan
;
Zhang, Jinhan
;
Wang, Liyan
;
Huang, Hao
;
Zhang, Zhibo
;
Song, Kai
;
Zhang, Xiaofei
Subjects:
Biological system modeling
;
Data integration
;
Data models
;
Feature extraction
;
gene-gene interaction
;
graph convolution neural network
;
lung adenocarcinoma
;
Lung cancer
;
Multi-omics data fusion
;
Neural networks
;
Task analysis
Is Part Of:
Access, IEEE, 2023, Vol.11, p.73568-73582
Description:
Compared to single-omics data, utilizing multi-omics data helps to gain a more comprehensive understanding of the occurrence and development of cancer, which emphasizes the necessity of developing efficient multi-omics data fusion approaches. In this study, a novel framework based on graph convolution neural networks with a progressively helical multi-omics data fusion strategy, named phMFGCN, is proposed to effectively integrate multiple omics data. To demonstrate the effectiveness of our framework in addressing the challenges of multi omics data fusion, phMFGCN and other widely-used machine learning methods conducted comparative experiments on predicting gene-gene interactions in lung adenocarcinoma. The results illustrated that phMFGCN outperforms other models with an accuracy of 97.94%. Additionally, 506 new gene-gene interactions predicted by this framework have been validated in databases such as BioGrid. Finally, it was used to perform gene function prediction, and the results were inconsistent with other existing research, for examples: Sam68, DHX9, and HNRNPK were involved in regulating multiple lung adenocarcinoma related pathways simultaneously. All these results demonstrate the universality of phMFGCN for different clinical tasks and it can provide reference target genes or gene-gene interactions for cancer mechanism research and treatment research in clinical practice.
Publisher:
IEEE
Language:
English
Identifier:
DOI: 10.1109/ACCESS.2023.3296474
Source:
IEEE Open Access Journals
This feature requires javascript
This feature requires javascript
Back to results list
This feature requires javascript
This feature requires javascript
Searching Remote Databases, Please Wait
Searching for
in
scope:(TDTS),scope:(SFX),scope:(TDT),scope:(SEN),primo_central_multiple_fe
Show me what you have so far
This feature requires javascript
This feature requires javascript