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

Effects of ambient wind on droplet deposition uniformity in orchard air-assisted sprayers

环境风对果园气辅喷雾器液滴沉积均匀性的影响

Xu, Tao; Li, Xue; Ding, Longpeng; Qi, Yannan; Lu, Haocheng; Xiao, Wen; Lv, Xiaolan; Li, Jingbin

Facilitating circularity of end-of-life photovoltaic in China with environmental benefits and costs informed by a high-resolution waste map

利用高分辨率废弃物地图,在环境效益和成本方面促进中国废旧光伏组件的循环利用。

Wang, Chen; Tian, Peipei; Zuo, Jian; Zhong, Honglin; Liu, Xi; Liu, Hailiang; Ma, Longpeng; Wang, Peng; Feng, Kuishuang; Li, Jiashuo

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning

利用机器学习识别与泌乳相关的基因特征,以预测乳腺癌患者的预后和治疗反应

Zhao, Jinfeng; Li, Longpeng; Wang, Yaxin; Huo, Jiayu; Wang, Jirui; Xue, Huiwen; Cai, Yue

Effectiveness of early cancer screening and diagnosis in urban China

中国城市地区早期癌症筛查和诊断的有效性

Liu, Yong; Rui, Yuanyuan; Zhao, Zhao; Zhang, Yu; Luo, Longpeng; Wu, Lei; Wei, Qingfeng

JCHAIN: A Prognostic Marker Based on Pan-Cancer Analysis to Inhibit Breast Cancer Progression.

JCHAIN:一种基于泛癌分析的预后标志物,用于抑制乳腺癌进展。

Zhao Jinfeng, Chen Wanquan, Li Longpeng, Zhang Zhibin, Wang Yaxin

State-of-the-art wearable sensors for cardiovascular health: a review

用于心血管健康的先进可穿戴传感器:综述

Xie, Hongwei; Yang, Longpeng; Jiang, Binbin; Huang, Zhenlong; Lin, Yuan

Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer

结合机器学习和实验验证,识别与乳腺癌中多种程序性细胞死亡相关的预后特征

Li, Longpeng; Zhao, Jinfeng; Wang, Yaxin; Zhang, Zhibin; Chen, Wanquan; Wang, Jirui; Cai, Yue

Integration of Bioinformatics and Machine Learning to Identify CD8+ T Cell-Related Prognostic Signature to Predict Clinical Outcomes and Treatment Response in Breast Cancer Patients

整合生物信息学和机器学习技术以识别CD8+ T细胞相关预后特征,从而预测乳腺癌患者的临床结果和治疗反应

Wu, Baoai; Li, Longpeng; Li, Longhui; Chen, Yinghua; Guan, Yue; Zhao, Jinfeng

Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response.

结合机器学习和实验验证来识别与细胞凋亡相关的预后特征,以预测乳腺癌肿瘤微环境和治疗反应

Li Longpeng, Li Longhui, Wang Yaxin, Wu Baoai, Guan Yue, Chen Yinghua, Zhao Jinfeng

[Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning]

【基于机器学习的体内电子顺磁共振谱分类及辐射剂量预测研究】

Xiong, Guangwei; Chen, Bo; Ma, Lei; Jia, Longpeng; Chen, Shunian; Wu, Ke; Ning, Jing; Zhu, Bin; Guo, Junwang