Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Anjun Ma"'
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
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Alzheimer’s Disease (AD) pathology has been increasingly explored through single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST). However, the surge in data demands a comprehensive, user-frie
Externí odkaz:
https://doaj.org/article/e6e54afecfdd4b6fb96b19646677f238
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
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
Autor:
Faith H. Brennan, Yang Li, Cankun Wang, Anjun Ma, Qi Guo, Yi Li, Nicole Pukos, Warren A. Campbell, Kristina G. Witcher, Zhen Guan, Kristina A. Kigerl, Jodie C. E. Hall, Jonathan P. Godbout, Andy J. Fischer, Dana M. McTigue, Zhigang He, Qin Ma, Phillip G. Popovich
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-20 (2022)
Here the authors show, using microglia-specific depletion techniques and single cell transcriptomics, that optimal repair after murine spinal cord injury (SCI) requires microglia.
Externí odkaz:
https://doaj.org/article/c71897bb4488416ab0aceb965a3c158b
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-5 (2022)
Single-cell multi-omics (scMulti-omics) has brought transformative insights into immuno-oncology, demonstrating success in describing novel immune subsets and defining important regulators of antitumor immunity. Here, we give examples of how scMulti-
Externí odkaz:
https://doaj.org/article/ed13ffe732524069b1a534d566072dbb
Autor:
Yi Jiang, Ruheng Wang, Jiuxin Feng, Junru Jin, Sirui Liang, Zhongshen Li, Yingying Yu, Anjun Ma, Ran Su, Quan Zou, Qin Ma, Leyi Wei
Publikováno v:
Advanced Science, Vol 10, Iss 11, Pp n/a-n/a (2023)
Abstract Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep hypergraph learning framework for the prediction of peptid
Externí odkaz:
https://doaj.org/article/588f2336ca0b4bc2a753111de279c44d
Autor:
No-Joon Song, Carter Allen, Anna E. Vilgelm, Brian P. Riesenberg, Kevin P. Weller, Kelsi Reynolds, Karthik B. Chakravarthy, Amrendra Kumar, Aastha Khatiwada, Zequn Sun, Anjun Ma, Yuzhou Chang, Mohamed Yusuf, Anqi Li, Cong Zeng, John P. Evans, Donna Bucci, Manuja Gunasena, Menglin Xu, Namal P. M. Liyanage, Chelsea Bolyard, Maria Velegraki, Shan-Lu Liu, Qin Ma, Martin Devenport, Yang Liu, Pan Zheng, Carlos D. Malvestutto, Dongjun Chung, Zihai Li
Publikováno v:
Journal of Hematology & Oncology, Vol 15, Iss 1, Pp 1-18 (2022)
Abstract Background Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) through direct lysis of infected lung epithelial cells, which releases damage-associated molecular patterns and induces a pro-
Externí odkaz:
https://doaj.org/article/0be369a340ab436a8618080570e5a87b
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 20, Iss , Pp 3053-3058 (2022)
Cis-regulatory motif (motif for short) identification and analyses are essential steps in detecting gene regulatory mechanisms. Deep learning (DL) models have shown substantial advances in motif prediction. In parallel, intuitive and integrative web
Externí odkaz:
https://doaj.org/article/1e1dc45f5c824573b9466336c31f5ed9