Browsing by Author "Huang B"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Nphos: Database and Predictor of Protein N-phosphorylation.
    (Oxford University Press, 2024-04-10) Zhao M-X; Ding R-F; Chen Q; Meng J; Li F; Fu S; Huang B; Liu Y; Ji Z-L; Zhao Y; Xue Y
    Protein N-phosphorylation is widely present in nature and participates in various biological processes. However, current knowledge on N-phosphorylation is extremely limited compared to that on O-phosphorylation. In this study, we collected 11,710 experimentally verified N-phosphosites of 7344 proteins from 39 species and subsequently constructed the database Nphos to share up-to-date information on protein N-phosphorylation. Upon these substantial data, we characterized the sequential and structural features of protein N-phosphorylation. Moreover, after comparing hundreds of learning models, we chose and optimized gradient boosting decision tree (GBDT) models to predict three types of human N-phosphorylation, achieving mean area under the receiver operating characteristic curve (AUC) values of 90.56%, 91.24%, and 92.01% for pHis, pLys, and pArg, respectively. Meanwhile, we discovered 488,825 distinct N-phosphosites in the human proteome. The models were also deployed in Nphos for interactive N-phosphosite prediction. In summary, this work provides new insights and points for both flexible and focused investigations of N-phosphorylation. It will also facilitate a deeper and more systematic understanding of protein N-phosphorylation modification by providing a data and technical foundation. Nphos is freely available at http://www.bio-add.org/Nphos/ and http://ppodd.org.cn/Nphos/.
  • Thumbnail Image
    Item
    Understanding consumers' continuance intention to watch streams: A value-based continuance intention model
    (Frontiers Media S.A., 2023-03-01) Jia X; Pang Y; Huang B; Hou F; Xie T
    INTRODUCTION: Live stream-watching has become increasingly popular worldwide. Consumers are found to watch streams in a continuous manner. Despite its popularity, there has been limited research investigating why consumers continue to watch streams. Previously, the expectation-confirmation theory (ECT) has been widely adopted to explain users' continuance intention. However, most current ECT-based models are theoretically incomplete, since they only consider the importance of perceived benefits without considering users' costs and sacrifices. In this paper, we propose a value-based continuance intention model (called V-ECM), and use it to investigate factors influencing consumers' continuance intention to watch streams. METHODS: Our hypotheses were tested using an online survey of 1,220 consumers with continuance stream-watching experiences. RESULTS: Results indicate that perceived value, a process of an overall assessment between users' perceived benefits and perceived sacrifices, is proved to be a better variable than perceived benefits in determining consumers' continuance watching intention. Also, compared with other ECT-based models, V-ECM is a more comprehensive model to explain and predict consumers' continuance intention. DISCUSSION: V-ECM theoretically extends ECT-based studies, and it has potential to explain and predict other continuance intentions in online or technology-related contexts. In addition, this paper also discusses practical implications for live streaming platforms with regards to their design, functions and marketing.