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Ziwei Yin on Muck Rack

Ziwei Yin

As seen in: Nature, PLOS, Frontiers, MDPI, PNAS, BioRxiv, Cell Press, Future Medicine
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Articles

Deciphering the platinum resistance heterogeneity landscape of Ovarian cancer through integrated single-cell and spatial transcriptomics data

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By Ganghua Zhang, Guanjun Chen, Jianing Fang, Yiqi Tan, Ziwei Yin, Jingxin Yang, Le Li Zeng, Xiangyang Zeng, Biyao Jiang, Songshu Xiao, Yuxing Zhu, Ke Cao, Wenzhi Deng, Zhijing Yin, Jingyu Ou
| Nature Verified
Abstract High tumor heterogeneity and platinum resistance are key drivers of poor prognosis in ovarian cancer (OV), yet platinum resistance heterogeneity within the OV tumor microenvironment remains incompletely characterized. Using five algorithms (AUCell, UCell, singscore, ssGSEA, AddModuleScore), we calculated a single-cell platinum resistance score (PRS) and stratified malignant cells into three PRS-based subtypes, followed by intercellular communication and pseudotime trajectory analyses.
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Artificial intelligence in the early diagnosis of prostate cancer: from multimodal imaging to liquid biopsy

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By Jun Xie, Ziwei Yin, Qing Zhou, Ruisong Gao, Zerui Qiu
| Frontiers
Abstract Prostate cancer (PCa) is the most prevalent malignant tumor in the urogenital system among men worldwide. Due to its subtle early symptoms and strong tumor heterogeneity, traditional diagnostic methods relying on a single prostate-specific antigen (PSA) initial screening and subjective imaging evaluations often lead to high false positives, overt biopsies, and missed small lesions.
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Combined transcriptomic, connectivity, and activity profiling of the medial amygdala using highly amplified multiplexed in situ hybridization (hamFISH)

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By Mathew Edwards, Ziwei Yin, Risa Sueda, Alina Gubanova
| BioRxiv
Abstract In situ transcriptomic technologies provide a promising avenue to link gene expression, connectivity, and physiological properties of neural cell types. Commercialized methods that allow the detection of hundreds of genes in situ, however, are expensive and therefore typically used for generating unimodal reference data rather than for resource-intensive multimodal analyses. A major bottleneck is the lack of a routine means to efficiently generate cell type data.
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