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Ie Ming Shih
Researcher at Johns Hopkins University
Publications - 401
Citations - 40438
Ie Ming Shih is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Ovarian cancer & Serous fluid. The author has an hindex of 97, co-authored 378 publications receiving 35329 citations. Previous affiliations of Ie Ming Shih include Howard Hughes Medical Institute & MedStar Washington Hospital Center.
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T cell-inflamed phenotype and increased Foxp3 expression in infiltrating T-cells of mismatch-repair deficient endometrial cancers.
TL;DR: The increased number of FoxP3 + regulatory T cells in mismatch repair-deficient endometrial cancers suggests that combination therapy by targeting both regulatory T cells and immune checkpoints may be warranted to improve clinical efficacy.
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Low-grade serous carcinoma of the ovary displaying a macropapillary pattern of invasion.
TL;DR: The finding of macropapillae within lymph nodes supports the interpretation that the macropAPillary component is another manifestation of invasion in LGSC.
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Gene expression signatures differentiate adenocarcinoma of lung and breast origin in effusions
Ben Davidson,Helene Tuft Stavnes,Björn Risberg,Jahn M. Nesland,Jeremias Wohlschlaeger,Yanqin Yang,Ie Ming Shih,Tian Li Wang +7 more
TL;DR: The aim of the present study was to compare the global gene expression patterns of metastases from these 2 malignancies to expand and improve the diagnostic panel of biomarkers currently available for their differential diagnosis, as well as to define type-specific biological targets.
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Knowledge-guided multi-scale independent component analysis for biomarker identification
Li Chen,Jianhua Xuan,Chen Wang,Ie Ming Shih,Yue Joseph Wang,Zhen Zhang,Eric P. Hoffman,Robert Clarke +7 more
TL;DR: The results show that the knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification and shows promising results to infer novel biomarkers for ovarian cancer and extend current knowledge.
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Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks
Ye Tian,Bai Zhang,Eric P. Hoffman,Robert Clarke,Zhen Zhang,Ie Ming Shih,Jianhua Xuan,David M. Herrington,Yue Wang +8 more
TL;DR: This work formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions.