ELMO: An Efficient Logistic Regression-Based Multi-Omic Integrated Analysis Method for Breast Cancer Intrinsic Subtypes-Reference-Cited by-同舟云学术

ELMO: An Efficient Logistic Regression-Based Multi-Omic Integrated Analysis Method for Breast Cancer Intrinsic Subtypes

Published:2020 Issue: Volume:8 Page:5121-5130
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Zhang Yexian^ORCID,Shi Ruoyao^ORCID,Chen Chaorong^ORCID,Duan Meiyu^ORCID,Liu Shuai^ORCID,Ren Yanjiao^ORCID,Huang Lan^ORCID,Dai Xiaofeng^ORCID,Zhou Fengfeng^ORCID

Funder

National Natural Science Foundation of China

Jilin Provincial Key Laboratory of Big Data Intelligent Computing

Education Department of Jilin Province

Jilin University

BioknowMedAI Institute

Fundamental Research Funds for the Central Universities

National Science and Technology Major Project

Natural Science Foundation of Jiangsu Province

Six Talent Peaks Project in Jiangsu Province

Technology Development Funding of Wuxi

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8948470/08935338.pdf?arnumber=8935338

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3. Evaluation and comparison of multi-omics data integration methods for cancer subtyping;PLOS Computational Biology;2021-08-12

4. Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine;IEEE Access;2020