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- 저자 : Caiyin Wang (검색결과 4 건)
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Wang Caiyin,Cui Lin,Li Hong 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.4
Because the accuracy of traditional sentiment orientation identification algorithm is not high under Q&A community, this paper proposes a new method based on two-level conditional random field improved by particle swarm optimization algorithm for emotion tendency recognition under Q&A community. The proposed method adopts particle swarm optimization algorithm to train two-level conditional random field model, and applies the trained conditional random field model to recognize emotion orientation of question-answer pairs in Q&A community. Experiments were performed on Yahoo! Answers data set and results show that the proposed two-level conditions random field improved by particle swarm optimization algorithm has a higher precision rate, recall rate and F1 value at the micro average and macro average aspects compared with Hidden Markov Model, Max-Entropy Markov Model, Support Vector Machine and traditional condition random domain model, which prove the proposed two-level conditions random field improved by particle swarm optimization algorithm is a more effective method to recognize emotion orientation of question-answer pairs in Q&A community.
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An Improved PSO Algorithm Based CommunityTopic Refinement Strategy for Social Network
Lin Cui,Caiyin Wang,Xiaoyin Wu 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.1
Aiming at the division roughness of topic classification existing in the most online social networks community, the improved particle swarm optimization algorithm is applied to refine community topics and concepts of community seeds and community topic are also introduced. In this paper, first of all, the explicit links existing in the community are mined, and the basic community structure is constructed, then the community content is deeply analyzed, according to implicit feature between nodes under online community, community topic categories are elaborately refined until structure is stable. Experiments show that this proposed algorithm can accelerate the convergence of the node and greatly improves the topic mining accuracy of online social network compared with the state-of-art CR2NDAS model and PLSA model.
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Lin Cui,Dechang Pi,Caiyin Wang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.5
In recent years, topic detection has become a hot research point of the social network, which can be very good to find the key factors from the massive information and thus discover the topics. The traditional label propagation-based topic discovery algorithm (LPA) is widely concerned because of its approximate linear time complexity and there is no need to define the target function. However, LPA algorithm has the uncertainty and the randomness, which affects the accuracy and the stability of the topic discovery. In this paper, a method for clustering label words based on mutual information analysis is presented to find the current topic. Firstly, through filtering the stop words and extracting keywords with TF-IDF, topic words are been extracted out, and then a common word matrix is built, a topic discovery algorithm based on mutual information and label clustering is put forward. Finally, extensive experiments on two real datasets validate the effectiveness of the proposed MI-LC (Mutual information-Label clustering) algorithm against other well-established methods LPA and LDA in terms of running time, NMI value and perplexity value.
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Shi Si,Gu Huijie,Xu Jinyuan,Sun Wan,Liu Caiyin,Zhu Tong,Wang Juan,Gao Furong,Zhang Jieping,Ou Qingjian,Jin Caixia,Xu Jingying,Chen Hao,Li Jiao,Xu Guotong,Tian Haibin,Lu Lixia 생화학분자생물학회 2023 Experimental and molecular medicine Vol.55 No.-
Excessive osteoclast activation, which depends on dramatic changes in actin dynamics, causes osteoporosis (OP). The molecular mechanism of osteoclast activation in OP related to type 1 diabetes (T1D) remains unclear. Glia maturation factor beta (GMFB) is considered a growth and differentiation factor for both glia and neurons. Here, we demonstrated that Gmfb deficiency effectively ameliorated the phenotype of T1D-OP in rats by inhibiting osteoclast hyperactivity. In vitro assays showed that GMFB participated in osteoclast activation rather than proliferation. Gmfb deficiency did not affect osteoclast sealing zone (SZ) formation but effectively decreased the SZ area by decreasing actin depolymerization. When GMFB was overexpressed in Gmfb-deficient osteoclasts, the size of the SZ area was enlarged in a dose-dependent manner. Moreover, decreased actin depolymerization led to a decrease in nuclear G-actin, which activated MKL1/SRF-dependent gene transcription. We found that pro-osteoclastogenic factors (Mmp9 and Mmp14) were downregulated, while anti-osteoclastogenic factors (Cftr and Fhl2) were upregulated in Gmfb KO osteoclasts. A GMFB inhibitor, DS-30, targeting the binding site of GMFB and Arp2/3, was obtained. Biocore analysis revealed a high affinity between DS-30 and GMFB in a dose-dependent manner. As expected, DS-30 strongly suppressed osteoclast hyperactivity in vivo and in vitro. In conclusion, our work identified a new therapeutic strategy for T1D-OP treatment. The discovery of GMFB inhibitors will contribute to translational research on T1D-OP.
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