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Start Over Author "Cui, Na" Publisher springer science and business media llc
41 results on '"Cui, Na"'

6. Kernel risk-sensitive mean p-power loss based hyper-graph regularized robust extreme learning machine and its semi-supervised extension for sample classification

Author

Rong Zhu, Jin-Xing Liu, Zhen-Xin Niu, Liang-Rui Ren, Cui-Na Jiao, and Juan Wang

Subjects
business.industry, Computer science, Supervised learning, Similarity measure, Machine learning, computer.software_genre, Multiclass classification, Statistical classification, Artificial Intelligence, Robustness (computer science), Kernel (statistics), Feature (machine learning), Artificial intelligence, business, computer, Extreme learning machine
Abstract

Extreme learning machine (ELM) has fast learning speed and perfect performance, at the same time, ELM provides a unified learning framework with a widespread type of feature mappings which can be applied in multiclass classification applications directly. These advantages make ELM become one of the best classification algorithms, and ELM has attracted great attention in supervised learning and semi-supervised learning. However, noise and outliers of data are usually existed in the real world, which will affect the performance of ELM. To improve the robustness and classification performance of ELM, we propose the Kernel Risk-Sensitive Mean p-power Loss Based Hyper-graph Regularized Robust Extreme Learning Machine (KRP-HRELM) method. On the one side, as a nonlinear similarity measure defined in the reproducing kernel space, the kernel risk-sensitive mean p-power loss (KRP) can effectively weaken the negative effects caused by noise and outliers. Therefore, the KRP is introduced into ELM to enhance its robustness. Then, the application of hyper-graph can help the ELM to explore higher-order geometric structures among more sampling points, thereby obtaining more comprehensive data information. In addition, to obtain a more sparsity network model, the L2,1-norm is used to constrain the output weight. On the other side, improving the practical application ability of KRP-HRELM is also the focus of our research, so KRP-HRELM is extended to semi-supervised learning, which is called the semi-supervised KRP-HRELM (SS-KRP-HRELM). Notably, the results of the robustness experiment have proved that our method has extraordinary robustness. At the same time, by using four evaluation measures such as accuracy, recall, precision, and F1-measure, to evaluate the classification results, we can find that our method has obtained better classification performance than other advanced methods.

Published
2021

22. MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

Author

Tian-Ru Wu, Jin-Xing Liu, Ying-Lian Gao, Meng-Meng Yin, Cui-Na Jiao, and Xiang-Zhen Kong

Subjects
Hepatoblastoma, Matrix completion, Computer science, Gaussian, 0206 medical engineering, Weight K Nearest Known Neighbors, Value (computer science), 02 engineering and technology, lcsh:Computer applications to medicine. Medical informatics, Machine learning, computer.software_genre, Biochemistry, Cross-validation, Matrix decomposition, 03 medical and health sciences, Kernel (linear algebra), symbols.namesake, Matrix (mathematics), Risk Factors, Structural Biology, Humans, Gene Regulatory Networks, Genetic Predisposition to Disease, MiRNA-disease association prediction, lcsh:QH301-705.5, Molecular Biology, 030304 developmental biology, 0303 health sciences, business.industry, Methodology Article, Applied Mathematics, Matrix factorization, Retinoblastoma, Reproducibility of Results, Computer Science Applications, MicroRNAs, lcsh:Biology (General), ROC Curve, Kernel (image processing), Area Under Curve, symbols, lcsh:R858-859.7, Artificial intelligence, business, computer, Algorithms, 020602 bioinformatics
Abstract

Background MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations. Results The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the fivefold cross-validation, with an AUC of 0.9569 (0.0005). Conclusions The AUC value of MCCMF is higher than other advanced methods in the fivefold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

Published
2020

24. Cross-sectional study for the clinical application of extracorporeal membrane oxygenation in Mainland China, 2018

Author

Cheng, Wei, primary, Ma, Xu-Dong, additional, Su, Long-Xiang, additional, He, Huai-Wu, additional, Wang, Lu, additional, Tang, Bo, additional, Du, Wei, additional, Zhou, Yuan-kai, additional, Wang, Hao, additional, Cui, Na, additional, Long, Yun, additional, Liu, Da-Wei, additional, Guo, Yan-Hong, additional, Wang, Ye, additional, Shan, Guang-Liang, additional, Zhou, Xiang, additional, Zhang, Shu-Yang, additional, and Zhao, Yu-Pei, additional

Published
2020
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28. A phase Ib, open-label, dose-escalation study of the safety and pharmacology of taselisib (GDC-0032) in combination with either docetaxel or paclitaxel in patients with HER2-negative, locally advanced, or metastatic breast cancer

Author

Abramson, Vandana G., primary, Oliveira, Mafalda, additional, Cervantes, Andrés, additional, Wildiers, Hans, additional, Patel, Manish R., additional, Bauer, Todd M., additional, Bedard, Philippe L., additional, Becerra, Carlos, additional, Richey, Stephen, additional, Wei, Michael C., additional, Reyner, Eric, additional, Bond, John, additional, Cui, Na, additional, Wilson, Timothy R., additional, Moore, Heather M., additional, Saura, Cristina, additional, and Krop, Ian E., additional

Published
2019
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