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20 results on '"Wang, Jingning"'

4. Research on Intelligent Anti-jamming Communication with Transform Domain Communication System

Author

Liu, Meng, Wang, Jingning, Liu, Lizhe, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Liu, Xin, editor, Na, Zhenyu, editor, Wang, Wei, editor, Mu, Jiasong, editor, and Zhang, Baoju, editor

Published
2019
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5. Research on Irregular Carrier Aggregation in Complex Electromagnetic Environment

Author

Wang, Jingning, Liu, Meng, Akan, Ozgur, Series Editor, Bellavista, Paolo, Series Editor, Cao, Jiannong, Series Editor, Coulson, Geoffrey, Series Editor, Dressler, Falko, Series Editor, Ferrari, Domenico, Series Editor, Gerla, Mario, Series Editor, Kobayashi, Hisashi, Series Editor, Palazzo, Sergio, Series Editor, Sahni, Sartaj, Series Editor, Shen, Xuemin (Sherman), Series Editor, Stan, Mircea, Series Editor, Xiaohua, Jia, Series Editor, Zomaya, Albert Y., Series Editor, Li, Cheng, editor, and Mao, Shiwen, editor

Published
2018
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9. Interferon-γ induces tumor resistance to anti-PD-1 immunotherapy by promoting YAP phase separation

Author

Yu, Man, primary, Peng, Zhengxin, additional, Qin, Min, additional, Liu, Yang, additional, Wang, Jingning, additional, Zhang, Cai, additional, Lin, Jiaming, additional, Dong, Tianqi, additional, Wang, Lulu, additional, Li, Shasha, additional, Yang, Yongqin, additional, Xu, Shan, additional, Guo, Wencong, additional, Zhang, Xiao, additional, Shi, Mingjun, additional, Peng, Huiming, additional, Luo, Xianwen, additional, Zhang, Huixia, additional, Zhang, Li, additional, Li, Yan, additional, Yang, Xiang-Ping, additional, and Sun, Shuguo, additional

Published
2021
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10. Systemic In Silico Screening in Drug Discovery for Coronavirus Disease (COVID-19) with an Online Interactive Web Server

Author

Xu, Chi, primary, Ke, Zunhui, additional, Liu, Chuandong, additional, Wang, Zhihao, additional, Liu, Denghui, additional, Zhang, Lei, additional, Wang, Jingning, additional, He, Wenjun, additional, Xu, Zhimeng, additional, Li, Yanqing, additional, Yang, Yanan, additional, Huang, Zhaowei, additional, Lv, Panjing, additional, Wang, Xin, additional, Han, Dali, additional, Li, Yan, additional, Qiao, Nan, additional, and Liu, Bing, additional

Published
2020
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11. K value selection method for test data similarity division based on K-Means algorithm

Author

Wang Jingning, Wang Dongxu, Hu Jing, and Xiwei Zhang

Subjects
Determining the number of clusters in a data set, Similarity (network science), Computer science, Range (statistics), k-means clustering, Scale (descriptive set theory), Division (mathematics), Algorithm, Selection (genetic algorithm), Test data
Abstract

The scale of test data can not be underestimated in the analysis of large-scale integrated circuit test,for the characteristics of K-Means algorithm the similarity of large amounts of data can be processed. It is convenient for subsequent operations to be processed according to the similarity such as compression, encoding, etc.. The selection of its center and the determination of the cluster number (K value) are the core of the K-Means algorithm, the selection of the K value is difficult to estimate. This paper has divided targeted and similarity for the generated exhaustive or pseudo-random test data. The K-means algorithm is combined with the constraints proposed in this paper to constrain the generation of suitable K values, and select appropriate range of K value. The experimental results based on a large number of random data have shown that when using the method in the text, the range of K values is selected in the 4-8 categories, the trend of each index is gentle, and the similarity between data is well.

Published
2019
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14. Systemic In SilicoScreening in Drug Discovery for Coronavirus Disease (COVID-19) with an Online Interactive Web Server

Author

Xu, Chi, Ke, Zunhui, Liu, Chuandong, Wang, Zhihao, Liu, Denghui, Zhang, Lei, Wang, Jingning, He, Wenjun, Xu, Zhimeng, Li, Yanqing, Yang, Yanan, Huang, Zhaowei, Lv, Panjing, Wang, Xin, Han, Dali, Li, Yan, Qiao, Nan, and Liu, Bing

Abstract

The emergence of the new coronavirus (nCoV-19) has impacted human health on a global scale, while the interaction between the virus and the host is the foundation of the disease. The viral genome codes a cluster of proteins, each with a unique function in the event of host invasion or viral development. Under the current adverse situation, we employ virtual screening tools in searching for drugs and natural products which have been already deposited in DrugBank in an attempt to accelerate the drug discovery process. This study provides an initial evaluation of current drug candidates from various reports using our systemic in silico drug screening based on structures of viral proteins and human ACE2 receptor. Additionally, we have built an interactive online platform (https://shennongproject.ai/) for browsing these results with the visual display of a small molecule docked on its potential target protein, without installing any specialized structural software. With continuous maintenance and incorporation of data from laboratory work, it may serve not only as the assessment tool for the new drug discovery but also an educational web site for the public.

Published
2020
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15. A new multi-node cooperative interference detection and recognition algorithm based on svm

Author

Zhu Chen, Wang Jingning, and He Zhanlin

Subjects
Computer science, business.industry, Node (networking), SIGNAL (programming language), Pattern recognition, computer.software_genre, Interference (wave propagation), Field (computer science), Support vector machine, Computer Science::Sound, Computer Science::Computer Vision and Pattern Recognition, Ranking SVM, Feature (machine learning), Data mining, Artificial intelligence, Recognition algorithm, business, computer
Abstract

Support vector machine (SVM), as a machine learning method based on data, is a theory of machine learning's law based on data in a limited number of data. We introduce the framework of multi-node cooperative interference detection and recognition algorithm, supply SVM to this field and realize cooperation among each node by transmitting interference signal's feature parameters. Based on simulation result, this method makes it possible to improve system's detection and recognition performance in the case of reducing cooperative detection and recognition's cost.

Published
2014
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20. Association between glioma and neurodegenerative diseases risk: a two-sample bi-directional Mendelian randomization analysis.

Author

Liu Y, Chen Y, Gao M, Luo J, Wang Y, Wang Y, Gao Y, Yang L, Wang J, and Wang N

Abstract

Background: Earlier observational studies have demonstrated a correlation between glioma and the risk of neurodegenerative diseases (NDs), but the causality and direction of their associations remain unclear. The objective of this study was to ascertain the causal link between glioma and NDs using Mendelian randomization (MR) methodology., Methods: Genome-wide association study (GWAS) data were used in a two-sample bi-directional MR analysis. From the largest meta-analysis GWAS, encompassing 18,169 controls and 12,488 cases, summary statistics data on gliomas was extracted. Summarized statistics for NDs, including Alzheimer's disease (AD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) were obtained from the GWAS of European ancestry. Inverse variance weighted (IVW) method was elected as the core MR approach with weighted median (WM) method and MR-Egger method as complementary methods. In addition, sensitivity analyses were performed. A Bonferroni correction was used to correct the results., Results: Genetically predicted glioma had been related to decreased risk of AD. Specifically, for all glioma (IVW: OR = 0.93, 95% CI = 0.90-0.96, p = 4.88 × 10 -6 ) and glioblastoma (GBM) (IVW: OR = 0.93, 95% CI = 0.91-0.95, p = 5.11 × 10 -9 ). We also found that genetically predicted all glioma has a suggestive causative association with MS (IVW: OR = 0.90, 95% CI = 0.81-1.00, p = 0.045). There was no evidence of causal association between glioma and ALS or PD. According to the results of reverse MR analysis, no discernible causal connection of NDs was found on glioma. Sensitivity analyses validated the robustness of the above associations., Conclusion: We report evidence in support of potential causal associations of different glioma subtypes with AD and MS. More studies are required to uncover the underlying mechanisms of these findings., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Chen, Gao, Luo, Wang, Wang, Gao, Yang, Wang and Wang.)

Published
2024
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