H
Hongzhi Li
Researcher at City of Hope National Medical Center
Publications - 62
Citations - 1945
Hongzhi Li is an academic researcher from City of Hope National Medical Center. The author has contributed to research in topics: DNA repair & Cancer cell. The author has an hindex of 17, co-authored 62 publications receiving 1166 citations. Previous affiliations of Hongzhi Li include Microsoft & Columbia University.
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Journal ArticleDOI
Targeting FTO Suppresses Cancer Stem Cell Maintenance and Immune Evasion
Rui Su,Lei Dong,Yangchan Li,Min Gao,Li Han,Mark Wunderlich,Xiaolan Deng,Hongzhi Li,Yue Huang,Lei Gao,Chenying Li,Zhicong Zhao,Sean Robinson,Brandon Tan,Ying Qing,Xi Qin,Emily Prince,Jun Xie,Hanjun Qin,Wei Li,Chao Shen,Jie Sun,Prakash Kulkarni,Hengyou Weng,Huilin Huang,Zhenhua Chen,Bin Zhang,Xiwei Wu,Mark Olsen,Markus Müschen,Guido Marcucci,Guido Marcucci,Ravi Salgia,Ling Li,Ling Li,Amir T. Fathi,Zejuan Li,James C. Mulloy,Minjie Wei,David Horne,Jianjun Chen,Jianjun Chen +41 more
TL;DR: It is shown that genetic depletion and pharmacological inhibition of FTO dramatically attenuate leukemia stem/initiating cell self-renewal and reprogram immune response by suppressing expression of immune checkpoint genes, especially LILRB4.
Proceedings ArticleDOI
Rethinking Classification and Localization for Object Detection
TL;DR: In this paper, the authors proposed a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression, achieving an accuracy of +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet101 backbones, respectively.
Posted Content
Rethinking Classification and Localization for Object Detection
TL;DR: A Double-Head method is proposed, which has a fully connected head focusing on classification and a convolution head for bounding box regression, and it is found that fc-head has more spatial sensitivity than conv-head.
Journal ArticleDOI
Joint Social and Content Recommendation for User-Generated Videos in Online Social Network
TL;DR: A joint social-content recommendation framework to suggest users which videos to import or re-share in the online social network is designed and results demonstrate the effectiveness of the approach and show that the approach can substantially improve the recommendation accuracy.
Journal ArticleDOI
Hint1 is a haplo-insufficient tumor suppressor in mice.
TL;DR: Deletion of Hint1 in mice enhances both spontaneous tumor development and susceptibility to tumor induction by DMBA in this mouse model of mammary carcinogenesis.