日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Machine learning model on multi-omics data enables risk stratification and identifies molecular heterogeneity and therapeutic targets in glioblastoma

基于多组学数据的机器学习模型能够对胶质母细胞瘤进行风险分层,并识别分子异质性和治疗靶点。

Zhang, Zhenyu; Wang, Zilong; Li, Ran; Pei, Dongling; Liu, Jingdian; Qiu, Yuning; Liu, Zaoqu; Wang, Minkai; Ma, Zeyu; Duan, Wenchao; Wang, Weiwei; Yan, Jing; Guo, Yang; Liu, Haoran; Li, Wenyuan; Yu, Yinhui; Chen, Te; Ma, Caoyuan; Yu, Miaomiao; Fu, Jing; Su, Dingyuan; Li, Sen; Geng, Haotian; Yu, Bin; Zhen, Yingwei; Chen, Ruokun; Sun, Qiuchang; Zhao, Yuanshen; Duan, Jingxian; Zheng, Hairong; Liang, Dong; Liu, Xianzhi; Li, Zhi-Cheng; Ji, Yuchen; Yan, Dongming

Multimodal fusion of radio-pathology and proteogenomics identify integrated glioma subtypes with prognostic and therapeutic opportunities.

放射病理学和蛋白质基因组学的多模态融合可识别具有预后和治疗机会的整合型胶质瘤亚型

Liu Zaoqu, Wu Yushuai, Xu Hui, Wang Minkai, Weng Siyuan, Pei Dongling, Chen Shuang, Wang WeiWei, Yan Jing, Cui Li, Duan Jingxian, Zhao Yuanshen, Wang Zilong, Ma Zeyu, Li Ran, Duan Wenchao, Qiu Yuning, Su Dingyuan, Li Sen, Liu Haoran, Li Wenyuan, Ma Caoyuan, Yu Miaomiao, Yu Yinhui, Chen Te, Fu Jing, Zhen YingWei, Yu Bin, Ji Yuchen, Zheng Hairong, Liang Dong, Liu Xianzhi, Yan Dongming, Han Xinwei, Wang Fubing, Li Zhi-Cheng, Zhang Zhenyu

Near infrared II excitation conjugated polymers-based phototheranostic nanoplatform for hyperthermia-enhanced ferroptosis and immunotherapy

基于近红外II激发共轭聚合物的光疗诊断纳米平台,用于热疗增强铁死亡和免疫治疗

Chen, Yingying; Sun, Ying; Qiu, Yuning; Li, Mingfei; Chen, Pengfei; Li, Bijun; Cai, Xintong; Sun, Pengfei; Li, Daifeng; Guo, Ruixia

MRI-based machine learning reveals proteasome subunit PSMB8-mediated malignant glioma phenotypes through activating TGFBR1/2-SMAD2/3 axis.

基于 MRI 的机器学习揭示了蛋白酶体亚基 PSMB8 通过激活 TGFBR1/2-SMAD2/3 轴介导的恶性胶质瘤表型

Pei Dongling, Ma Zeyu, Qiu Yuning, Wang Minkai, Wang Zilong, Liu Xianzhi, Zhang Long, Zhang Zhenyu, Li Ran, Yan Dongming

ADAR1 Promotes the Progression and Temozolomide Resistance of Glioma Through p62-Mediated Selective Autophagy

ADAR1通过p62介导的选择性自噬促进胶质瘤的进展和替莫唑胺耐药性

Zhang, Yuyan; Guo, Huiling; Bu, Jiahao; Wang, Weiwei; Wang, Li; Liu, Zhibo; Qiu, Yuning; Wang, Qimeng; Zhou, Lijuan; Liu, Xianzhi; Ma, Liwei; Wei, Jianwei

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

利用放射组学分析对具有不同生物学通路活性的岛叶弥漫性胶质瘤进行分层

Duan, Wenchao; Wang, Zilong; Ma, Zeyu; Zheng, Hongwei; Li, Yinhua; Pei, Dongling; Wang, Minkai; Qiu, Yuning; Duan, Mengjiao; Yan, Dongming; Ji, Yuchen; Cheng, Jingliang; Liu, Xianzhi; Zhang, Zhenyu; Yan, Jing

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images

利用深度学习技术,从全切片病理图像中对成人型弥漫性胶质瘤进行神经病理学家级别的综合分类

Wang, Weiwei; Zhao, Yuanshen; Teng, Lianghong; Yan, Jing; Guo, Yang; Qiu, Yuning; Ji, Yuchen; Yu, Bin; Pei, Dongling; Duan, Wenchao; Wang, Minkai; Wang, Li; Duan, Jingxian; Sun, Qiuchang; Wang, Shengnan; Duan, Huanli; Sun, Chen; Guo, Yu; Luo, Lin; Guo, Zhixuan; Guan, Fangzhan; Wang, Zilong; Xing, Aoqi; Liu, Zhongyi; Zhang, Hongyan; Cui, Li; Zhang, Lan; Jiang, Guozhong; Yan, Dongming; Liu, Xianzhi; Zheng, Hairong; Liang, Dong; Li, Wencai; Li, Zhi-Cheng; Zhang, Zhenyu

Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma

IDH野生型胶质母细胞瘤风险分层中放射组学磁共振成像表型的生物学基础

Guan, Fangzhan; Wang, Zilong; Qiu, Yuning; Guo, Yu; Pei, Dongling; Wang, Minkai; Xing, Aoqi; Liu, Zhongyi; Yu, Bin; Cheng, Jingliang; Liu, Xianzhi; Ji, Yuchen; Yan, Dongming; Yan, Jing; Zhang, Zhenyu

Diffusion tensor imaging-based machine learning for IDH wild-type glioblastoma stratification to reveal the biological underpinning of radiomic features

基于扩散张量成像的机器学习方法用于IDH野生型胶质母细胞瘤分层,以揭示放射组学特征的生物学基础

Wang, Zilong; Guan, Fangzhan; Duan, Wenchao; Guo, Yu; Pei, Dongling; Qiu, Yuning; Wang, Minkai; Xing, Aoqi; Liu, Zhongyi; Yu, Bin; Zheng, Hongwei; Liu, Xianzhi; Yan, Dongming; Ji, Yuchen; Cheng, Jingliang; Yan, Jing; Zhang, Zhenyu

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas

多参数磁共振成像的放射组学特征可预测儿童低级别胶质瘤的分子亚型

Liu, Zhen; Hong, Xuanke; Wang, Linglong; Ma, Zeyu; Guan, Fangzhan; Wang, Weiwei; Qiu, Yuning; Zhang, Xueping; Duan, Wenchao; Wang, Minkai; Sun, Chen; Zhao, Yuanshen; Duan, Jingxian; Sun, Qiuchang; Liu, Lin; Ding, Lei; Ji, Yuchen; Yan, Dongming; Liu, Xianzhi; Cheng, Jingliang; Zhang, Zhenyu; Li, Zhi-Cheng; Yan, Jing