Title: Plasma proteins related to the state of depression: a case-control study based on proteomics data of pregnant women

Authors: Yuhao Feng; Jinman Zhang; Zengyue Zheng; Chenyu Xing; Min Li; Guanghong Yan; Ping Chen; Dingyun You; Ying Wu

Addresses: Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510080, China ' National Health Commission's Key Laboratory for Healthy Births in Western China, Department of Obstetrics and Gynecology, First People's Hospital of Yunnan Province, Kunming 650032, China ' Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510080, China ' NHC Key Laboratory of Periconception Health Birth in Western China, School of Public Health, Kunming Medical University, Kunming 650500, China ' NHC Key Laboratory of Periconception Health Birth in Western China, School of Public Health, Kunming Medical University, Kunming 650500, China ' NHC Key Laboratory of Periconception Health Birth in Western China, School of Public Health, Kunming Medical University, Kunming 650500, China ' NHC Key Laboratory of Periconception Health Birth in Western China, School of Public Health, Kunming Medical University, Kunming 650500, China ' Institute of Biomedical Engineering, Kunming Medical University, Kunming, 650500, China ' Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510080, China

Abstract: Prenatal and postpartum emotional changes in pregnant women in early pregnancy are of great significance to the physical and mental health of mothers and infants. To identify factors related to this, we conducted this study to identify feature proteins that cause maternal depression. Boruta algorithm (BA), recursive partition algorithm (RPA), regularised random forest (RRF) algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and genetic algorithm (GA) were used to select features. Extreme gradient boosting (XGBoost), back propagation neural network (BPNN), support vector machine (SVM), random forest (RF), and logistic regression (LR) were selected to construct the predictive models. All models showed a good performance in predicting, with the mean AUC (the area under the receiver operating curve) exceeding 80%. Features will provide clues to prevent depression in pregnant women and improve the physical and mental health of mothers and babies.

Keywords: pregnant women; depression; proteomics; biomarkers; feature selection.

DOI: 10.1504/IJDMB.2025.147044

International Journal of Data Mining and Bioinformatics, 2025 Vol.29 No.3, pp.313 - 337

Received: 23 Jun 2023
Accepted: 09 Feb 2024
Published online: 10 Jul 2025 *

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