Integration of Network Biology and Imaging to Study Cancer Phenotypes and Responses-Reference-Cited by-同舟云学术

Integration of Network Biology and Imaging to Study Cancer Phenotypes and Responses

Published:2014-11-01 Issue:6 Volume:11 Page:1009-1019
ISSN:1545-5963
Container-title:IEEE/ACM Transactions on Computational Biology and Bioinformatics
language:
Short-container-title:IEEE/ACM Trans. Comput. Biol. and Bioinf.

Author:

Tian Ye,Ye Shaozhen,Clarke Robert,Wang Chao,Zhang Bai,Wang Yue,Albanese Chris,Wang Sean S.,Zhang Zhen,Rodriguez Olga C.,Petricoin III Emanuel,Shih Ie-Ming,Chan Daniel,Avantaggiati Maria,Yu Guoqiang

Funder

National Institutes of Health

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Genetics,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/8857/6980148/06857391.pdf?arnumber=6857391

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Identification of potential prognostic markers for lung adenocarcinoma using comprehensive analysis;Molecular Medicine Reports;2023-06-20

2. Machine learning based identification of hub genes in renal clear cell carcinoma using multi-omics data;Methods;2022-11

3. Identification of co-expression hub genes for ferroptosis in kidney renal clear cell carcinoma based on weighted gene co-expression network analysis and The Cancer Genome Atlas clinical data;Scientific Reports;2022-03-21

4. Retinal vessel classification to predict diabetic retinopathy and retinitis pigmentosa using a deep learning pipeline;EIGHTH INTERNATIONAL CONFERENCE NEW TRENDS IN THE APPLICATIONS OF DIFFERENTIAL EQUATIONS IN SCIENCES (NTADES2021);2022

5. Network analysis with biological data of cancer patients: A scoping review;Journal of Biomedical Informatics;2021-08