Four‐gene signature based on machine learning filtration could predict prognosis of patients with breast cancer

Author:

Liu Bo12ORCID,Wang Huina1ORCID,Wang Xin3ORCID,Long Junqi1ORCID,Zhuang Xujie1ORCID,Ji Xinchan1ORCID,Zhu Nian1ORCID,Li Jinmeng1ORCID,Gao Ting4ORCID,Zhang Xuehui5ORCID,Yu Jiangyong6ORCID,Zhao Shuangtao7ORCID

Affiliation:

1. School of Software Engineering, Faculty of Information Technology Beijing University of Technology Beijing china

2. School of Mathematical and Computational Sciences Massey University Palmerston North New Zealand

3. Department of Basic Medicine The College of Nursing and Health of Zhengzhou University Zhengzhou Henan Province China

4. Department of Disease and Infection Control, National Cancer Center/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College. Beijing China

5. Department of Blood Transfusion Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy Tianjin China

6. Department of Medical Oncology Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences Beijing China

7. Department of Thoracic Surgery Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University Beijing China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Reference49 articles.

1. Journal of Translational Medicine

2. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

3. Recognition of ischaemia and infection in diabetic foot ulcer: A deep convolutional neural network based approach

4. DFU_SPNet: A stacked parallel convolution layers based CNN to improve Diabetic Foot Ulcer classification

5. Fusion of handcrafted and deep convolutional neural network features for effective identification of diabetic foot ulcer;Das S. K.;Concurrency and Computation: Practice and Experience,2021

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