YUE LI

Dec 4, 2024 | en.people.cn | YUE LI

Tea culture blooms in Cambridge and London universities: A taste of China, a bridge to the world Tea culture often embodies the unique lifestyles and spiritual pursuits of different nations and regions. Serving as a remarkable bridge between China and the UK, it fosters intercultural dialogue and mutual appreciation.

Nov 18, 2024 | mdpi.com | Hanlin Lai |YUE LI |Feifei Deng |jin Li

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.

Oct 25, 2024 | biorxiv.org | Michael Huang |YUE LI

AbstractAdvancements in single-cell transcriptomics methods have resulted in a wealth of single-cell RNA sequencing (scRNA-seq) data. Methods to learn cell representation from atlas-level scRNA-seq data across diverse tissues can shed light into cell functions implicated in diseases such as cancer. However, integrating large-scale and heterogeneous scRNA-seq data is challenging due to the disparity of cell-types and batch effects.

Oct 25, 2024 | mdpi.com | Yiming Wu |Kai Zhu |Chu Wang |YUE LI

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.

Oct 24, 2024 | biorxiv.org | Pei Liu |Xiao Liang |YUE LI |Jiawei Luo

AbstractSystematic investigation of high-order molecular interactions can deepen our understanding of the mechanisms underlying biological systems. However, effectively capturing both multilinear and nonlinear relationships to accurately identify the complex triplet relationships remains a challenge. In this paper, we present a novel Convolutinal Neural Tensor Completion (ConvNTC) to model triplet network connections. ConvNTC consists of a multilinear module and nonlinear module.