Yingying Li

Jan 6, 2025 | onlinelibrary.wiley.com | Yinan Li |Xiu Huang |Qingqing Qiao |Yingying Li

Conflict of Interest The authors declare no conflict of interest. Supporting Information Filename Description adma202414237-sup-0001-SuppMat.docx48.4 MB Supporting Information References 1, , , , , , , , , , , , , , , , , , , , , , , , Lancet. 2020, 395, 200. 2, , , Int. J. Mol. Sci. 2019, 20. 3, , , , Crit. Rev. Microbiol. 2013, 39, 229. 4, Nature. 2002, 420, 885. 5, , Immunity. 2014, 40, 463. 6, , , Cell. 2006, 124, 783. 7, , Curr. Opin. Immunol. 2008, 20, 420.

Oct 24, 2024 | mdpi.com | Yingying Li |Guoliang Zhu |Jing Wang |Junjie Yu

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Aug 29, 2024 | onlinelibrary.wiley.com | Yinan Li |Xiu Huang |Yingying Li |Qingqing Qiao

1 Introduction Current drug therapies for tumors primarily rely on chemotherapy, targeted therapy, and immunotherapy. However, there is currently no research on the development of new therapies aimed at inducing tumor fibrosis by mimicking organ fibrosis. Fibrosis is characterized by the formation of a dense network of collagen fibers and represents a crucial mechanism of body tissue repair mode.[1] Current research suggests that tumor fibrosis plays a dual role in tumor growth and metastasis.

Nov 27, 2023 | onlinelibrary.wiley.com | Jin Feng Li |Yingying Li |Qin Li |Lu Sun

Supporting Information As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.

Oct 13, 2023 | mdpi.com | Jinghong Zhang |Yingying Li |Bowen Liu |Hao Chen

1. IntroductionThe issue of class imbalance has been a challenge in the fields of data mining, computer vision, and machine learning over the past decade [1,2]. The class imbalance problem is prominently visible within machine learning datasets, where the distribution of instances across diverse classes is drastically uneven. Classical machine learning models tend to be biased towards majority classes. Conversely, minority classes can hardly determine the parameters of the classifier.