Fisher Discrimination Regularized Robust Coding Based on a Local Center for Tumor Classification-Reference-Cited by-同舟云学术

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Fisher Discrimination Regularized Robust Coding Based on a Local Center for Tumor Classification

Published:2018-06-14 Issue:1 Volume:8 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Li Weibiao,Liao Bo,Zhu Wen,Chen Min,Li Zejun,Wei Xiaohui,Peng Lihong,Huang Guohua,Cai Lijun,Chen HaoWen

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/s41598-018-27364-7.pdf

Reference43 articles.

1. Desai, A. N. & Jere, A. Next Generation Sequencing: ready for the clinics? Clin Genet 81, 503–510 (2012).

2. Li, X. A fast and exhaustive method for heterogeneity and epistasis analysis based on multi-objective optimization. Bioinformatics (2017).

3. Sirin, U., Erdogdu, U., Polat, F., Tan, M. & Alhajj, R. Effective gene expression data generation framework based on multi-model approach. Artificial Intelligence in Medicine 70, 41 (2016).

4. Gu, C. et al. Global network random walk for predicting potential human lncRNA-disease associations. Sci Rep 7, 12442 (2017).

5. Li, W. et al. Maxdenominator Reweighted Sparse Representation for Tumor Classification. Scientific Reports 7 (2017).

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1. A sandwich-type electrochemical immunosensor using polythionine/AuNPs nanocomposites as label for ultrasensitive detection of carcinoembryonic antigen;Materials Express;2019-08-01

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