Association between Alzheimer's disease pathologic products and age and a pathologic product-based diagnostic model for Alzheimer's disease by Weizhe Zhen, Yu Wang, Hongjun Zhen, Weihe Zhang, Wen Shao, Yu Sun, Yanan Qiao, Shuhong Jia, Zhi Zhou, Yuye Wang, Leian Chen, Jiali Zhang, Dantao Peng, Dantao Peng

“…In the non-AD group, the trend of pathologic product levels with age was consistently opposite to that of the AD group. We finally screened the optimal AD diagnostic model (AUC=0.959) based on the results of correlation analysis and by using the Xgboost algorithm and SVM algorithm.ConclusionIn a novel finding, we observed that Tau protein and Aβ had opposite trends with age in both the AD and non-AD groups. …”
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Anti-EBV: Artificial intelligence driven predictive modeling for repurposing drugs as potential antivirals against Epstein-Barr virus by Hiteshi Vaidya, Sakshi Gautam, Manoj Kumar

“…The top-performing model was used to screen approved drugs from DrugBank, identifying potential repurposed drugs namely arzoxifene, succimer, abemaciclib and many more. …”
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Machine learning models in enhancing prediction of health-related indices among older adults: A scoping review by Raoof Nopour

“…Objective: This scoping review aims to investigate machine learning models in predicting health-related indices among older adults. …”
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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.ObjectiveThis study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.MethodsA retrospective analysis was conducted on 847 pediatric patients with AH. …”
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A Computationally Efficient Model Predictive Control Energy Management Strategy for Hybrid Vehicles Considering Driving Style by Yalian Yang, Yuqi Chen, Changdong Liu

“…The driving-style adaptive Pontryagin’s minimum principle for model predictive control (DSA-PMP-MPC) algorithm was designed as a real-time energy management strategy for Hybrid Electric Vehicles (HEVs). …”
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Multi-Comparison of Different Ocular Imaging Modality-based Deep Learning Models for Visually Significant Cataract Detection by Jocelyn Hui Lin Goh, BEng, Xiaofeng Lei, MSc, Miao-Li Chee, MPH, Yiming Qian, PhD, Marco Yu, PhD, Tyler Hyungtaek Rim, MD, PhD, Simon Nusinovici, PhD, David Ziyou Chen, MBBS, FRCOphth, Kai Hui Koh, BSc, Samantha Min Er Yew, BSc, Yibing Chen, BEng, Victor Teck Chang Koh, MBBS, MMed, Charumathi Sabanayagam, MD, PhD, Tien Yin Wong, MD, PhD, Xinxing Xu, PhD, Rick Siow Mong Goh, PhD, Yong Liu, PhD, Ching-Yu Cheng, MD, PhD, Yih-Chung Tham, PhD

“…A community study data set of nonmydriatic retinal photos (N = 310 eyes) was used for external testing of the retinal model. Methods: We developed 3 single-modality DL models (retinal, slit beam, and diffuse anterior segment photos) and 4 ensemble models (4 different combinations of the 3 single-modality models) to detect visually significant cataract (VSC). …”
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An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes by Na An, Zhongwen Lu, Yang Li, Bing Yang, Shaozhen Ji, Xu Dong, Zhaoliang Ding

“…To build a diagnostic model for UC, we applied 113 combinations of 12 machine learning algorithms. …”
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Artificial intelligence in primary aldosteronism: current achievements and future challenges by Yisi Xu, Benjin Liu, Xuqi Huang, Xudong Guo, Ning Suo, Shaobo Jiang, Hanbo Wang

“…Recent advances in artificial intelligence (AI) are reshaping the diagnostic and therapeutic of primary aldosteronism (PA). For screening, machine learning models integrate multidimensional data to improve the efficiency of PA detection, facilitating large-scale population screening. …”
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Predicting postoperative malnutrition in patients with oral cancer: development of an XGBoost model with SHAP analysis and web-based application by Lixia Kuang, Lixia Kuang, Jingya Yu, Yunyu Zhou, Yu Zhang, Yu Zhang, Guangman Wang, Guangman Wang, Fangmin Zhang, Grace Paka Lubamba, Grace Paka Lubamba, Xiaoqin Bi, Xiaoqin Bi

“…The dataset was divided into a training set (70%) and a validation set (30%). Predictive models were developed via four supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost). …”
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Investigation of the influence of the heterogeneous structure of concrete on its strength by V.M. Volchuk, M.A. Kotov, Ye G. Plakhtii, O.A. Tymoshenko, O.H. Zinkevych

“…This approach allowed screening out low-sensitivity structure features from the multifractal spectrum. …”
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Developing an Uncrewed Aerial Vehicle (UAV)-Based Prediction Model for the Rice Harvest Index Using Machine Learning by Zhaoyang Pan, Zhanhua Lu, Liting Zhang, Wei Liu, Xiaofei Wang, Shiguang Wang, Hao Chen, Haoxiang Wu, Weicheng Xu, Youqiang Fu, Xiuying He

“…Based on the above characteristics, this study used a variety of machine learning algorithms to construct a harvest index prediction model. …”
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Machine learning-based prediction model for lung ischemia-reperfusion injury: insights from disulfidptosis-related genes by Yanpeng Zhang, Yanpeng Zhang, Yanpeng Zhang, Jingyang Sun, Jingyang Sun, Jingyang Sun, Yihan Lin, Yihan Lin, Yihan Lin, Rongxuan Jiang, Rongxuan Jiang, Rongxuan Jiang, Niuniu Dong, Niuniu Dong, Niuniu Dong, Huanhuan Dong, Huanhuan Dong, Huanhuan Dong, Peng Li, Peng Li, Peng Li, Jinteng Feng, Jinteng Feng, Jinteng Feng, Zijiang Zhu, Guangjian Zhang, Guangjian Zhang, Guangjian Zhang

“…Multiple machine learning algorithms (Generalized Linear Model, Support Vector Machine, and Random Forest) were applied for joint screening, leading to the construction of a predictive model. …”
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Analysis of vehicle and pedestrian detection effects of improved YOLOv8 model in drone-assisted urban traffic monitoring system. by Huili Dou, Sirui Chen, Fangyuan Xu, Yuanyuan Liu, Hongyang Zhao

“…The multi-scale feature fusion module enhances the model's detection ability for targets of different sizes by combining feature maps of different scales; the improved non-maximum suppression algorithm effectively reduces repeated detection and missed detection by optimizing the screening process of candidate boxes. …”
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Multilayer Network Modeling for Brand Knowledge Discovery: Integrating TF-IDF and TextRank in Heterogeneous Semantic Space by Peng Xu, Rixu Zang, Zongshui Wang, Zhuo Sun

“…This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. …”
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Prognostic model identification of ribosome biogenesis-related genes in pancreatic cancer based on multiple machine learning analyses by Yuan Sun, Yan Li, Anlan Zhang, Tao Hu, Ming Li

“…Prognostic gene sets were screened using machine learning algorithms to construct a risk model, which was externally validated via GEO database. …”
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An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome by Wanyi Li, Hangyu Zhou, Yingxue Zou

“…This study used eight machine learning algorithms to construct predictive models. Recursive feature elimination with cross-validation is used to screen features, and cross-validation-based Bayesian optimization is used to filter the features used to find the optimal combination of hyperparameters for the model. …”
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Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma by LI Chaoqun, ZHENG Hongliang, HUANG Ping

“…Subsequently, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were employed to screen CARGs. A prognostic model of CARGs was constructed by incorporating 12 machine learning algorithms, and univariate and multivariate Cox regression analyses were applied to evaluate whether the 12 models as an independent prognostic factor or not. …”
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Establishment of an alternative splicing prognostic risk model and identification of FN1 as a potential biomarker in glioblastoma multiforme by Xi Liu, Jinming Song, Zhiming Zhou, Yuting He, Shaochun Wu, Jin Yang, Zhonglu Ren

“…The eleven genes (C2, COL3A1, CTSL, EIF3L, FKBP9, FN1, HPCAL1, HSPB1, IGFBP4, MANBA, PRKAR1B) were screened to develop an alternative splicing prognostic risk score (ASRS) model through machine learning algorithms. …”
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Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer by Erha Munai, Siwei Zeng, Ze Yuan, Dingyi Yang, Yong Jiang, Qiang Wang, Yongzhong Wu, Yunyun Zhang, Dan Tao

“…Four different machine learning (ML) algorithms were used to create prediction models for BMs in ES-SCLC patients. …”
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Molecular function validation and prognostic value analysis of the cuproptosis-related gene ferredoxin 1 in papillary thyroid carcinoma by Shiyue He, Wenzhong Peng, Xinyue Hu, Yong Chen

“…LASSO regression analyses were utilized to screen the optimal combination of cuproptosis-related genes for constructing a Cox proportional-hazards model, and the cuproptosis-related risk score (CRRS) was calculated to stratify PTC patients in prognosis. …”
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