Zobrazeno 1 - 10
of 235
pro vyhledávání: '"XIANGTAO LI"'
Publikováno v:
Communications Biology, Vol 7, Iss 1, Pp 1-16 (2024)
Abstract Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein functi
Externí odkaz:
https://doaj.org/article/7fcae50dc2884d479affc7ebbed527d7
Publikováno v:
Advanced Science, Vol 11, Iss 39, Pp n/a-n/a (2024)
Abstract The 3' untranslated regions (3'UTRs) of messenger RNAs contain many important cis‐regulatory elements that are under functional and evolutionary constraints. It is hypothesized that these constraints are similar to grammars and syntaxes in
Externí odkaz:
https://doaj.org/article/097b9b2913724daf9445e90c1e104e05
Autor:
Bosen Zhu, Ming Liu, Tianhao Mu, Wentao Li, Junqi Ren, Xiangtao Li, Yi Liang, Ziyi Yang, Yulin Niu, Shifu Chen, Junqiong Lin
Publikováno v:
Frontiers in Oncology, Vol 14 (2024)
The incidence of multiple primary tumors(MPTs) is on the rise in recent years, but patients having four or more primary tumors is still rare. Lynch syndrome (LS) patients have a high risk of developing MPTs. NGS sequencing could identify the genetic
Externí odkaz:
https://doaj.org/article/c90055d9721b4ef5a0cc6abbb5ccda40
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract In recent times, a new wave of scientific and technological advancements has significantly reshaped the global economic structure. This shift has redefined the role of regional innovation, particularly in its contribution to developing the G
Externí odkaz:
https://doaj.org/article/332acadb219f4b6d876d29d03ad347cd
Autor:
Haoran Zhu, Yuning Yang, Yunhe Wang, Fuzhou Wang, Yujian Huang, Yi Chang, Ka-chun Wong, Xiangtao Li
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-22 (2023)
Abstract RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, cu
Externí odkaz:
https://doaj.org/article/5ecbd87616af4963a49821f5a4f534a8
Publikováno v:
Advanced Science, Vol 11, Iss 16, Pp n/a-n/a (2024)
Abstract Single‐cell RNA sequencing (scRNA‐seq) is a robust method for studying gene expression at the single‐cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expressio
Externí odkaz:
https://doaj.org/article/8db091e69fdc4cd8b0d18db7c34926b9
Autor:
Fuzhou Wang, Hamid Alinejad‐Rokny, Jiecong Lin, Tingxiao Gao, Xingjian Chen, Zetian Zheng, Lingkuan Meng, Xiangtao Li, Ka‐Chun Wong
Publikováno v:
Advanced Science, Vol 10, Iss 33, Pp n/a-n/a (2023)
Abstract Single‐cell Hi‐C (scHi‐C) has made it possible to analyze chromatin organization at the single‐cell level. However, scHi‐C experiments generate inherently sparse data, which poses a challenge for loop calling methods. The existing
Externí odkaz:
https://doaj.org/article/f933729e4a8a4b3db973f60eaa529354
Autor:
Olutomilayo Olayemi Petinrin, Faisal Saeed, Muhammad Toseef, Zhe Liu, Shadi Basurra, Ibukun Omotayo Muyide, Xiangtao Li, Qiuzhen Lin, Ka-Chun Wong
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 21, Iss , Pp 2454-2470 (2023)
Cancer has received extensive recognition for its high mortality rate, with metastatic cancer being the top cause of cancer-related deaths. Metastatic cancer involves the spread of the primary tumor to other body organs. As much as the early detectio
Externí odkaz:
https://doaj.org/article/8ecaf083dbff4491ab38f6b67970e7ee
Autor:
Zhuohan Yu, Yanchi Su, Yifu Lu, Yuning Yang, Fuzhou Wang, Shixiong Zhang, Yi Chang, Ka-Chun Wong, Xiangtao Li
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-18 (2023)
A major challenge in analyzing scRNA-seq data arises from challenges related to dimensionality and the prevalence of dropout events. Here the authors develop a deep graph learning method called scMGCA based on a graph-embedding autoencoder that simul
Externí odkaz:
https://doaj.org/article/b10f4f1b28094157b30e70f064f4a6ae
Publikováno v:
Communications Biology, Vol 6, Iss 1, Pp 1-15 (2023)
An ensemble deep learning model (EDLM)-based protein-protein interaction (PPI) site identification method (EDLMPPI) accurately predicts PPIs through protein language models and outperforms state-of-the-art methods on multiple benchmark sets.
Externí odkaz:
https://doaj.org/article/8d452b20b34e49428bdb3a8435857d9e