Classification of lung cancer using ensemble-based feature selection and machine learning methods-Reference-Cited by-同舟云学术

Classification of lung cancer using ensemble-based feature selection and machine learning methods

Published:2015 Issue:3 Volume:11 Page:791-800
ISSN:1742-206X
Container-title:Molecular BioSystems
language:en
Short-container-title:Mol. BioSyst.

Author:

Cai Zhihua¹²³,Xu Dong⁴⁵⁶³,Zhang Qing⁷⁸⁹³,Zhang Jiexia¹⁰¹¹²³,Ngai Sai-Ming⁷⁸⁹³,Shao Jianlin¹²¹³¹⁴¹⁵³

Affiliation:

1. Affiliated Cancer Hospital of Guangzhou Medical University

2. Guangzhou

3. China

4. Department of Mathematics & Scientific Computing Key Laboratory of Shanghai Universities

5. Shanghai Normal University

6. Shanghai

7. School of Life Sciences and State (China) Key Laboratory of Agrobiotechnology

8. The Chinese University of Hong Kong

9. Hong Kong

10. China State Key Laboratory of Respiratory Disease and Guangzhou Institute of Respiratory Disease

11. The First Affiliated Hospital of Guangzhou Medical University

12. First Affiliated Hospital

13. School of Medicine

14. Zhejiang University

15. Hangzhou

Abstract

The ensemble-based feature selection method presents the merit of acquisition of more informative and compact features than those obtained by individual methods.

Publisher

Royal Society of Chemistry (RSC)

Subject

Molecular Biology,Biotechnology

Link

http://pubs.rsc.org/en/content/articlepdf/2015/MB/C4MB00659C

Reference74 articles.

1. The Changing Epidemiology of Lung Cancer

2. Changing Epidemiology of Small-Cell Lung Cancer in the United States Over the Last 30 Years: Analysis of the Surveillance, Epidemiologic, and End Results Database

3. Small-cell lung cancer

4. Small Cell Lung Cancer

5. Phase II Study of Efficacy and Safety of Bevacizumab in Combination With Chemotherapy or Erlotinib Compared With Chemotherapy Alone for Treatment of Recurrent or Refractory Non–Small-Cell Lung Cancer

Cited by 121 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. Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes;Computational Biology and Chemistry;2024-10

2. Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study;PLOS ONE;2024-09-10

3. Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia;Tissue Engineering Part A;2024-08-07

4. A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology;Briefings in Functional Genomics;2024-04-10

5. Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data—In Pursuit of Precision;Bioengineering;2024-03-26