Ying Li

3 weeks ago | nature.com | Weishen Pan |Zhenxing Xu |Qiannan Zhang |Ying Li |Fei Wang |Deep Hathi

Predicting treatment response is an important problem in real-world applications, where the heterogeneity of the treatment response remains a significant challenge in practice. Unsupervised machine learning methods have been proposed to address this challenge by clustering patients with similar electronic health record (EHR) data. However, they cannot guarantee coherent outcomes within the groups. Here, we propose Graph-Encoded Mixture Survival (GEMS) as a general machine learning framework to identify distinct predictive subphenotypes that guarantee coherent survival and baseline characteristics within each subphenotype. We apply our method to a real-world dataset of advanced non-small cell lung cancer (aNSCLC) patients receiving first-line immune checkpoint inhibitor (ICI) therapy to predict overall survival (OS). Our method outperforms baseline methods for predicting OS and identifies three reproducible subphenotypes associated with distinct baseline clinical characteristics and OS. Our results demonstrate that our method can provide insights in the heterogeneity of treatment response and potentially influence treatment selection. Response to cancer therapeutics is heterogenous making it hard predict despite advances in machine learning approaches. Here, the authors develop a graph neural network-based approach, Graph-Encoded Mixture Survival (GEMS), to identify ‘predictive subphenotypes’ of patients with similar baseline characteristics and survival outcomes of cancer patients using electronic health records to predict patient response to therapy.

1 month ago | mdpi.com | Nan Zhang |Ying Li |Xian Zhang |Micah A. Chrenek

4. Materials and Methods 4.1. Experimental DesignMice were divided into three groups: the Naïve group, the PBS+ONC group, and the TUDCA+ONC group. The Naïve group (12 mice) received no treatments. The PBS+ONC group (16 mice) received intraperitoneal (i.p.) injections of phosphate-buffered saline (PBS) and underwent unilateral optic nerve crush (ONC) surgery. The TUDCA + ONC group (15 mice) received i.p. injections of tauroursodeoxycholic acid (TUDCA) followed by unilateral ONC surgery.

Dec 18, 2024 | mdpi.com | Ying Li

Altmetric announcement Help format_quote Cite thumb_up ... Endorse Need Help? Find support for a specific problem in the support section of our website. Please let us know what you think of our products and services. Visit our dedicated information section to learn more about MDPI.

Dec 6, 2024 | onlinelibrary.wiley.com | Ying Li |Jiapeng Yang |Qian Zhang |Ke Zhang

Conflicts of Interest The authors declare no conflicts of interest. Supporting Information Filename Description pce15312-sup-0001-Figure_S1.pdf1 MB Figure S1. Collinearity analysis of P-type ATPase genes between Dendrobium officinale and four other plants, including A. thaliana, O. sativa, V. planifolia and D. chrysotoxum. pce15312-sup-0002-Figure_S2.tif341 KB Figure S2.

Dec 3, 2024 | nature.com | Ying Li |Yiran Li |Wei Wang |Yi Cao

AbstractWhile hydrogel-based flexible sensors find extensive applications in fields such as medicine and robotics, their performance can be hindered by the rapid evaporation of water, leading to diminished sensitivity and mechanical durability.