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Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification.

Z. Niu, L. Ren, R. Zhu, X. Kong, Y. Gao, and J. Liu. ICIC (2), volume 12837 of Lecture Notes in Computer Science, page 532-539. Springer, (2021)

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Ying Gao

Gao Ying

Ying Ying

Jianping Ying

Xiang Ying

Other publications of authors with the same name

Application of Graph Regularized Non-negative Matrix Factorization in Characteristic Gene Selection.D. Wang, Y. Gao, J. Liu, J. Yu, and C. Wen. ICIC (2), volume 9226 of Lecture Notes in Computer Science, page 601-611. Springer, (2015)MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction.Y. Wang, Y. Gao, J. Wang, J. Shang, and J. Liu. ISBRA, volume 13760 of Lecture Notes in Computer Science, page 1-8. Springer, (2022)M3HOGAT: A Multi-View Multi-Modal Multi-Scale High-Order Graph Attention Network for Microbe-Disease Association Prediction.S. Wang, J. Liu, F. Li, J. Wang, and Y. Gao. IEEE J. Biomed. Health Informatics, 28 (10): 6259-6267 (October 2024)Identify Complex Higher-Order Associations Between Alzheimer's Disease Genes and Imaging Markers Through Improved Adaptive Sparse Multi-view Canonical Correlation Analysis.Y. Wang, X. Kong, B. Guan, C. Zheng, and Y. Gao. ICIC (3), volume 14088 of Lecture Notes in Computer Science, page 324-334. Springer, (2023)DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization.J. Liu, M. Gao, Z. Cui, Y. Gao, and F. Li. BMC Bioinform., 22-S (3): 241 (2021)DSNPCMF: Predicting MiRNA-Disease Associations with Collaborative Matrix Factorization Based on Double Sparse and Nearest Profile.M. Yin, Z. Cui, J. Liu, Y. Gao, and X. Kong. IDMB, volume 1099 of Communications in Computer and Information Science, page 196-208. Springer, (2019)Spatial Domain Identification Based on Graph Attention Denoising Auto-encoder.Y. Gao, D. Zhang, C. Jiao, Y. Gao, and J. Liu. ICIC (3), volume 14088 of Lecture Notes in Computer Science, page 359-367. Springer, (2023)MKGSAGE: A Computational Framework via Multiple Kernel Fusion on GraphSAGE for Inferring Potential Disease-Related Microbes.S. Wang, J. Liu, B. Liu, L. Dai, F. Li, and Y. Gao. BIBM, page 648-653. IEEE, (2023)The computational prediction of drug-disease interactions using the dual-network L2,1-CMF method.Z. Cui, Y. Gao, J. Liu, J. Wang, J. Shang, and L. Dai. BMC Bioinform., 20 (1): 5 (December 2019)A review of recent advances in spatially resolved transcriptomics data analysis.Y. Gao, Y. Gao, J. Jing, F. Li, C. Zheng, and J. Liu. Neurocomputing, (2024)

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Disambiguation of "Gao, Ying-Lian"

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Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification.

Please choose a person to relate this publication to

Ying Gao

Gao Ying

Ying Ying

Jianping Ying

Xiang Ying

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Gao, Ying-Lian"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification.

Please choose a person to relate this publication to

Ying Gao

Gao Ying

Ying Ying

Jianping Ying

Xiang Ying

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
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APA
Chicago
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Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification.