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收藏分享 收藏 分享 收藏 分享 收藏 分享 收藏 分享 收藏 Community assessment of methods to deconvolve cellular composition from bulk gene expression
White, Brian S.; de Reynies, Aurelien; Newman, Aaron M.; Waterfall, Joshua J.; Lamb, Andrew; Petitprez, Florent; Lin, Yating; Yu, Rongshan; Guerrero-Gimenez, Martin E.; Domanskyi, Sergii; Monaco, Gianni; Chung, Verena; Banerjee, Jineta; Derrick, Daniel; Valdeolivas, Alberto; Li, Haojun; Xiao, Xu; Wang, Shun; Zheng, Frank; Yang, Wenxian; Catania, Carlos A.; Lang, Benjamin J.; Bertus, Thomas J.; Piermarocchi, Carlo; Caruso, Francesca P.; Ceccarelli, Michele; Yu, Thomas; Guo, Xindi; Bletz, Julie; Coller, John; Maecker, Holden; Duault, Caroline; Shokoohi, Vida; Patel, Shailja; Liliental, Joanna E.; Simon, Stockard; Saez-Rodriguez, Julio; Heiser, Laura M.; Guinney, Justin; Gentles, Andrew J. 分享 收藏 分享 收藏 ConTIG: Continuous representation learning on temporal interaction graphs重叠群: 时间交互图的连续表示学习Wang, Zihui; Yang, Peizhen; Fan, Xiaoliang; Yan, Xu; Wu, Zonghan; Pan, Shirui; Chen, Longbiao; Zang, Yu; Wang, Cheng; Yu, Rongshan 分享 收藏 分享 收藏 分享 收藏 分享 收藏 分享 收藏 Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer优先考虑结直肠癌单细胞转录组的预后相关亚群和个体化复发风险特征Tong, Mengsha; Lin, Yuxiang; Yang, Wenxian; Song, Jinsheng; Zhang, Zheyang; Xie, Jiajing; Tian, Jingyi; Luo, Shijie; Liang, Chenyu; Huang, Jialiang; Yu, Rongshan 分享 收藏 Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition ProteomicsHe, Qingzu; Zhong, Chuan-Qi; Li, Xiang; Guo, Huan; Li, Yiming; Gao, Mingxuan; Yu, Rongshan; Liu, Xianming; Zhang, Fangfei; Guo, Donghui; Ye, Fangfu; Guo, Tiannan; Shuai, Jianwei; Han, Jiahuai 分享 收藏 Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma多重成像质量细胞仪揭示了与黑色素瘤免疫治疗反应相关的独特肿瘤免疫微环境Xiao, Xu; Guo, Qian; Cui, Chuanliang; Lin, Yating; Zhang, Lei; Ding, Xin; Li, Qiyuan; Wang, Minshu; Yang, Wenxian; Kong, Yan; Yu, Rongshan 分享 收藏 A downsampling method enables robust clustering and integration of single-cell transcriptome data一种下采样方法可以对单细胞转录组数据进行可靠的聚类和整合Ren, Jun; Zhang, Quan; Zhou, Ying; Hu, Yudi; Lyu, Xuejing; Fang, Hongkun; Yang, Jing; Yu, Rongshan; Shi, Xiaodong; Li, Qiyuan 分享 收藏 分享 收藏 Application of individualized differential expression analysis in human cancer proteome个体化差异表达分析在人类癌症蛋白质组研究中的应用Liu, Yachen; Lin, Yalan; Yang, Wenxian; Lin, Yuxiang; Wu, Yujuan; Zhang, Zheyang; Lin, Nuoqi; Wang, Xianlong; Tong, Mengsha; Yu, Rongshan 分享 收藏 DAISM-DNNXMBD: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networksLin, Yating; Li, Haojun; Xiao, Xu; Zhang, Lei; Wang, Kejia; Zhao, Jingbo; Wang, Minshu; Zheng, Frank; Zhang, Minwei; Yang, Wenxian; Han, Jiahuai; Yu, Rongshan 分享 收藏 
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