Skip To Main Content
Zheng Yin on Muck Rack

Zheng Yin

As seen in: British Ecological Society (Wiley), Color Research & Application, Nature, Wiley, Wiley Online Library, PLOS, Frontiers, MDPI, Sexually Transmitted Infections, ACS Publications and
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Zheng

Contact Zheng, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Prenatal Limosilactobacillus reuteri Supplementation Shapes Breast Milk and Programs the Newborn Mouse Gut Metabolome

|
By Nhat Hoang, Salomea Giorgberidze, Zheng Yin
| Preprints.org
Submitted: 11 September 2026 Posted: 17 September 2026 You are already at the latest version Limosilactobacillus reuteri DSM 17938 feeding to healthy newborn mice modulates gut microbiota and boosts beneficial metabolites. We assessed whether DSM 17938 supplementation in pregnant dams alters breast milk (BM) metabolites and shapes the infant gut metabolomic profile, aimed to improve offspring immunity and overall health.
Open in Who Shared Issue with article?

CAMI-DM: Development and validation of a multi-algorithm model for in-hospital mortality risk prediction in diabetic patients with acute myocardial infarction — The China acute myocardial infarction registry

|
By Zheng Yin, Junxing Lv, Zuoxiang Wang
| PLOS
Loading metrics Open Access Peer-reviewed Research Article Citation: Wang Z, Yin Z, Lv J, Zhao S, Ba Z, Yang J, et al. (2026) CAMI-DM: Development and validation of a multi-algorithm model for in-hospital mortality risk prediction in diabetic patients with acute myocardial infarction — The China acute myocardial infarction registry. PLOS Digit Health 5(8): e0001529.
Open in Who Shared Issue with article?

Prediction of Carbon Dioxide Adsorption Performance of Covalent Organic Frameworks (COFs) Based on Machine Learning: Identification of Key Factors and Model Deployment Click to copy article link Article link copied!

|
By Zheng Yin, Zhiyang Jiang, Zhongke Jiang
| ACS Publications
3.1. Training and Construction of Machine Learning Models This study employed six machine learning models─XGBoost (XGB), Random Forest (RF), k -Nearest Neighbors (KNN), CatBoost, Gradient Boosting Decision Trees (GBDT), and Bagging─to predict the carbon dioxide adsorption capacity. All models were optimized via 5-fold cross-validation (5-fold CV) to mitigate overfitting. Table S3 lists the R2 , MAE, and RMSE for each fold of the 5-fold cross-validation for all six models.
Open in Who Shared Issue with article?
See all 314 licensed articles (317 Total)