Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Bingqiang Liu"'
Autor:
Yuzhou Chang, Jixin Liu, Yi Jiang, Anjun Ma, Yao Yu Yeo, Qi Guo, Megan McNutt, Jordan E. Krull, Scott J. Rodig, Dan H. Barouch, Garry P. Nolan, Dong Xu, Sizun Jiang, Zihai Li, Bingqiang Liu, Qin Ma
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-22 (2024)
Abstract Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics pro
Externí odkaz:
https://doaj.org/article/41ab4e48d4e6480da1c0f949ca4779ee
Autor:
Zhaoqian Liu, Yuhan Sun, Yingjie Li, Anjun Ma, Nyelia F. Willaims, Shiva Jahanbahkshi, Rebecca Hoyd, Xiaoying Wang, Shiqi Zhang, Jiangjiang Zhu, Dong Xu, Daniel Spakowicz, Qin Ma, Bingqiang Liu
Publikováno v:
Advanced Science, Vol 11, Iss 41, Pp n/a-n/a (2024)
Abstract Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a
Externí odkaz:
https://doaj.org/article/49b8bc32f29f40c6af2d4e9f5be81612
Publikováno v:
Meitan kexue jishu, Vol 52, Iss 5, Pp 176-190 (2024)
Thick coal seams are widely distributed in the world, which contain rich geological information and play an important role in the global carbon cycle. The genetic mechanism of thick coal seams has recently gained attention in coal geology research. I
Externí odkaz:
https://doaj.org/article/1af2684c764d4bc6988fd21471c02381
Autor:
Zhi Liu, Qing Yang, Bingqiang Liu, Chenhui Li, Xiaolei Shi, Yu Wei, Yuefeng Guan, Chunyan Yang, Mengchen Zhang, Long Yan
Publikováno v:
BMC Genomic Data, Vol 25, Iss 1, Pp 1-5 (2024)
Abstract Objectives Soybean is an important feed and oil crop in the world due to its high protein and oil content. China has a collection of more than 43,000 soybean germplasm resources, which provides a rich genetic diversity for soybean breeding.
Externí odkaz:
https://doaj.org/article/5dfc5a90b2284a5a8d91e43ed03af029
Publikováno v:
Frontiers in Genetics, Vol 15 (2024)
MotivationThe interaction between DNA motifs (DNA motif pairs) influences gene expression through partnership or competition in the process of gene regulation. Potential chromatin interactions between different DNA motifs have been implicated in vari
Externí odkaz:
https://doaj.org/article/6c36542491944406a0fed1ab92338ace
Autor:
Xiaoying Wang, Maoteng Duan, Jingxian Li, Anjun Ma, Gang Xin, Dong Xu, Zihai Li, Bingqiang Liu, Qin Ma
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-18 (2024)
Abstract Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we intro
Externí odkaz:
https://doaj.org/article/65356025a01e4df8b30bca9b8c924629
Publikováno v:
iScience, Vol 27, Iss 3, Pp 109294- (2024)
Summary: The noninvasive detection of pancreatic ductal adenocarcinoma (PDAC) remains an immense challenge. In this study, we proposed a robust, accurate, and noninvasive classifier, namely Multi-Omics Co-training Graph Convolutional Networks (MOCO-G
Externí odkaz:
https://doaj.org/article/11f73667cb794d79a0a0efdd4f7267bb
Autor:
Chenhui Li, Qing Yang, Bingqiang Liu, Xiaolei Shi, Zhi Liu, Chunyan Yang, Tao Wang, Fuming Xiao, Mengchen Zhang, Ainong Shi, Long Yan
Publikováno v:
Plants, Vol 13, Iss 9, p 1260 (2024)
Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datase
Externí odkaz:
https://doaj.org/article/3d333758c7e0423ca9ddd76a218d43f3
Autor:
Anjun Ma, Xiaoying Wang, Jingxian Li, Cankun Wang, Tong Xiao, Yuntao Liu, Hao Cheng, Juexin Wang, Yang Li, Yuzhou Chang, Jinpu Li, Duolin Wang, Yuexu Jiang, Li Su, Gang Xin, Shaopeng Gu, Zihai Li, Bingqiang Liu, Dong Xu, Qin Ma
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-18 (2023)
Single-cell multi-omics and deep learning could lead to the inference of biological networks across specific cell types. Here, the authors develop DeepMAPS, a deep learning, graph-based approach for cell-type specific network inference from single-ce
Externí odkaz:
https://doaj.org/article/1a148a7ce6274ae9bdb44a249d63b34b
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-13 (2022)
Single-cell RNA-seq data provide the opportunity to predict drug response in cancer while considering intratumour heterogeneity. Here, the authors develop a deep transfer learning framework - scDEAL - to predict single-cell drug responses in cancer b
Externí odkaz:
https://doaj.org/article/ce295af39449440f934d4c2d009ddaf5