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Ying Chen on Muck Rack

Ying Chen

As seen in: Arthritis & Rheumatology, Color Research & Application, Nature, Wiley, Wiley Online Library, International Journal of Nursing Studies, New Phytologist, PLOS, PLOS ONE, BMJ Open and
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Articles

Construction of a five-gene prognostic model involving T cell exhaustion and metabolic reprogramming for predicting hepatocellular carcinoma risk using LASSO: insights into immune infiltration and drug sensitivity

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By Shaogang Wang, Ying Chen, Yu Mao, Zhijian J. Chen, Xueqin Zhang, Tao Zhang
| Nature Verified
Abstract Hepatocellular carcinoma (HCC) ranks as one of the most common and deadly malignancies in the world. Its pathogenesis is closely correlated with T cell exhaustion and metabolic reprogramming, these two critical yet underexplored mechanisms can profoundly shape the tumor immune microenvironment (TIME) and drive disease progression.
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Depression, functional disability, and cardiovascular disease in adults with cardiovascular-kidney-metabolic syndrome stages 0–3: a nationally representative cohort study

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By Jianxing Chen, Yujia Zhou, Ying Chen, Cankun Yu, Duowen Zheng, Zicheng He
| Nature Verified
Abstract Cardiovascular-kidney-metabolic syndrome (CKM) has emerged as an integrated framework for identifying individuals at increased cardiovascular risk before clinical cardiovascular disease (CVD) develops. However, whether psychological and functional vulnerabilities further refine risk stratification in earlier CKM stages remains unclear.
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Group Shapley values, with application to bond recovery rate prediction

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By Jingyi Wang, Paolo Giudici, Ying Chen
| Nature Verified
Abstract Grouped feature attribution is increasingly important in high-dimensional applications, where predictors often belong to economically or scientifically meaningful categories and individual SHAP values can become too granular for reliable interpretation. This paper develops a formal inferential framework for assessing whether group-level SHAP-style attributions are statistically significant and complements it with a tree-based grouped implementation for ensemble models.
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