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

Changes of brain structure and structural covariance networks in Parkinson's disease with different sides of onset

帕金森病不同发病侧别患者的脑结构和结构协方差网络的变化

Xu, Tianqi; Deng, Zhihuai; Yu, Yinhui; Duan, Wenchao; Ma, Zeyu; Liu, Haoran; Li, Lianling; Zhang, Moxuan; Zhou, Siyu; Yang, Pengda; Qin, Xueyan; Zhang, Zhenyu; Meng, Fangang; Ji, Yuchen

Application of visualization three-dimensional medical imaging technology based on mobile platform in surgery of giant thyroid tumors: a case report

基于移动平台的三维可视化医学成像技术在巨大甲状腺肿瘤手术中的应用:病例报告

Wu, Zhen; Sun, Hailin; Li, Zhao; Chen, Shen; Han, Lin; Wang, Yongkui; Duan, Wenchao; Zhang, Wei

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

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

Upcycling Bread Waste into a Ag-Doped Carbon Material Applied to the Detection of Halogenated Compounds in Waters

将面包废料升级再造成银掺杂碳材料,并应用于水体中卤代化合物的检测

Duan, Wenchao; Fernández-Sánchez, César; Gich, Martí

Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities

扩散张量成像的深度学习特征可改善胶质瘤分层,并识别具有不同分子通路活性的风险组

Yan, Jing; Zhao, Yuanshen; Chen, Yinsheng; Wang, Weiwei; Duan, Wenchao; Wang, Li; Zhang, Shenghai; Ding, Tianqing; Liu, Lei; Sun, Qiuchang; Pei, Dongling; Zhan, Yunbo; Zhao, Haibiao; Sun, Tao; Sun, Chen; Wang, Wenqing; Liu, Zhen; Hong, Xuanke; Wang, Xiangxiang; Guo, Yu; Li, Wencai; Cheng, Jingliang; Liu, Xianzhi; Lv, Xiaofei; Li, Zhi-Cheng; Zhang, Zhenyu