Michael Cheng
Indiana University
8 Papers
Michael Cheng is an academic researcher from Indiana University. The author has contributed to research in topics: Medicine & Cancer. The author has an hindex of 3, co-authored 3 publications.
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Papers
Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications
Yanxia Wu,Michael Cheng,Shuo Huang,Zongxiang Pei,Yi Zuo,Jianxin Liu,Kai Yang,Qi Zhou,Jie Zhang,Honghai Hong,Daoqiang Zhang,Kun Huang,Liang Cheng,Wei Shao +13 more
TL;DR: A comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis, including color normalization, nuclei/tissue segmentation, and cancer diagnosis and prognosis.
Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma
Jun Cheng,Zhi Han,Zhi Han,Rohit Mehra,Wei Shao,Michael Cheng,Qianjin Feng,Dong Ni,Kun Huang,Kun Huang,Liang Cheng,Jie Zhang +11 more
TL;DR: In this paper, the authors used machine learning and H&E stained whole-slide images to distinguish TFE3-RCC from ccRCC and achieved high accuracy with areas under ROC curve ranging from 0.842 to 0.894.
Targeting Chromatin Effector Pygo2 to Enhance Immunotherapy in Prostate Cancer
Yini Zhu,Yun Zhao,Jiling Wen,Sheng Liu,Tianhe Huang,Ishita Hatial,Xiaoxia Peng,Hawraa Al Janabi,Gang Huang,Jackson Mittlesteadt,Michael Cheng,Atul Bhardwaj,Brandon L. Ashfeld,Kenneth R. Kao,Dean Y. Maeda,Xing Dai,Olaf Wiest,Brian S. J. Blagg,Xuemin Lu,Liang Cheng,Jun Wang,Xin Liu +21 more
TL;DR: Using transgenic mouse models of metastatic prostate adenocarcinoma, it is found that Pygo2 deletion decelerated tumor progression, diminished metastases, and extended survival, highlighting a promising path to improving immunotherapy with targeted therapy for lethal prostate cancer.
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Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer's Disease.
Travis S. Johnson,Shunian Xiang,Shunian Xiang,Tianhan Dong,Zhi Huang,Michael Cheng,Tianfu Wang,Kai Yang,Dong Ni,Kun Huang,Jie Zhang +10 more
TL;DR: In this article, the authors mined five large transcriptomic AD datasets for conserved gene co-expression module, then analyzed differential expression and differential coexpression within the modules between AD samples and controls.