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Yong M. Dai
Publications - 7
Citations - 88
Yong M. Dai is an academic researcher. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 1, co-authored 1 publications receiving 14 citations.
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Journal ArticleDOI
COMMD10 inhibits HIF1α/CP loop to enhance ferroptosis and radiosensitivity by disrupting Cu-Fe balance in hepatocellular carcinoma.
Mi Yang,Xixi Wu,Jinlong Hu,Yingqiao Wang,Yin Wang,Long Zhang,Weiqiang Huang,Xiaoqing Wang,Nan Li,Liwei Liao,Min Chen,Nan J. Xiao,Yong M. Dai,Huazhen Liang,Wenqi Huang,Lu Yuan,Hua Pan,Lu Li,Longhua Chen,Laiyu Liu,Li Liang,Jian Guan +21 more
TL;DR: In this paper , the authors investigated the role of copper in radiotherapy and revealed new targets and treatment strategies for overcoming radioresistance in hepatocellular carcinoma (HCC) patients.
Journal ArticleDOI
18F-FDG uptake for prediction EGFR mutation status in non-small cell lung cancer.
Jian Guan,Nan J. Xiao,Min Chen,Wen L. Zhou,Yao W. Zhang,Shuang Wang,Yong M. Dai,Lu Li,Yue Zhang,Qin Y. Li,Xiang Z. Li,Mi Yang,Hu B. Wu,Long H. Chen,Lai Y. Liu +14 more
TL;DR: The specific FDG uptake value could be considered to effectively predict EGFR mutation status of NSCLC patients by considering smoking history and primary tumor size when genetic tests are not available.
Journal ArticleDOI
Concordance Study of a 520-Gene Next-Generation Sequencing-Based Genomic Profiling Assay of Tissue and Plasma Samples
Minghui Wang,Xianshan Chen,Yong M. Dai,Duoguang Wu,Fang Liu,Zheng–qiang Yang,Baozhi Song,Lei Xie,Liang Yang,Weidi Zhao,Chenxu Zhang,Weixi Shen,Chengjuan Fan,Chong Teng,Xue Zhao,Naisheng Gao,Di Shang,Guofang Zhao,Tao Xin +18 more
TL;DR: Paired tumor and blood samples from a large cohort of patients spanning 20 tumor types demonstrated that the OncoScreen Plus is a reliable pan-cancer panel for the accurate detection of somatic variants and genomic signatures that could guide individualized treatment strategies to improve the care of patients with advanced cancer.
Journal ArticleDOI
Development and validation of a deep learning model to predict survival of patients with esophageal cancer
Chen Huang,Yong M. Dai,Qianshun Chen,Hongchao Chen,Yuan-Chian Lin,Jingyu Wu,Xunyu Xu,Xiao Song Chen +7 more
TL;DR: Deep learning neural networks have potential advantages over traditional linear models in prognostic assessment and treatment recommendations and may provide reliable information on individual survival and treatmentRecommendations for patients with esophageal cancer.
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Screening and identification of CNIH4 gene associated with cell proliferation in gastric cancer based on a large-scale CRISPR-Cas9 screening database DepMap.
TL;DR: Wang et al. as mentioned in this paper identified candidate genes associated with the proliferation and survival of gastric cancer cell through CRISPR-cas9 screening data, which may provide new therapeutic targets for Gastric cancer patients.