Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets by Kuoyuan Cheng, Laura Martin‐Sancho, Lipika R Pal, Yuan Pu, Laura Riva, Xin Yin, Sanju Sinha, Nishanth Ulhas Nair, Sumit K Chanda, Eytan Ruppin

“…Abstract Tremendous progress has been made to control the COVID‐19 pandemic caused by the SARS‐CoV‐2 virus. …”
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Screening risk factors for the occurrence of wedge effects in intramedullary nail fixation for intertrochanteric fractures in older people via machine learning and constructing a p... by Zhe Xu, Qiuhan Chen, Zhi Zhou, Jianbo Sun, Guang Tian, Chen Liu, Guangzhi Hou, Ruguo Zhang

“…The purpose of this study was to screen risk factors for the intraoperative V-effect in intertrochanteric fractures and to develop a clinical prediction model. …”
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Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL. …”
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Unlocking The Potential of Hybrid Models for Prognostic Biomarker Discovery in Oral Cancer Survival Analysis: A Retrospective Cohort Study by Leila Nezamabadi Farahani, Anoshirvan Kazemnejad, Mahlagha Afrasiabi, Leili Tapak

“…Concordance index (C-index), mean absolute error (MAE), mean squared error (MSE) and R-squares, were used to evaluate the performance of the models using selected features. Functional enrichment analysis was performed using DAVID database, and external validation utilized three independent datasets (GSE9844, GSE75538, GSE37991, GSE42743).Results: The findings indicated that the PSO-based method outperformed the GA-based method, achieving a smaller MAE (0.061) and MSE (0.005), R-square (0.99) and C-index (0.973), selecting 291 probes from 1069 screened. …”
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Cost-effectiveness analysis of MASLD screening using FIB-4 based two-step algorithm in the medical check-up by Mimi Kim, Huiyul Park, Eileen L. Yoon, Ramsey Cheung, Donghee Kim, Hye-Lin Kim, Dae Won Jun

“…We constructed a hybrid model of the decision tree model and Markov model to compare expected costs and quality-adjusted life-years (QALYs) between ‘screening’ and ‘no screening’ groups from healthcare system perspectives. …”
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The Utility of Follow-up Transthoracic Echocardiogram to Screen for Severe Portopulmonary Hypertension (POPH) in Patients Granted POPH Model for End-stage Liver Disease (MELD) Exce... by Kathryn T. del Valle, MD, Dana Kay, MD, Michael J. Krowka, MD, James R. Runo, MD, Corey Sadd, MD, Julie K. Heimbach, MD, Rodrigo Cartin-Ceba, MD, Hector R. Cajigas, MD, Charles D. Burger, MD, John E. Moss, MD, Hilary M. DuBrock, MD

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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…Then, a transportation accessibility calculation model is constructed using spatial syntax for secondary screening. …”
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Retrospective validation of the postnatal growth and retinopathy of prematurity criteria in a Chinese cohort by Li Li, Yanlin Gao, Yuhan Lu, Wei Chen, Mei Han

“…Application of the G-ROP prediction model can improve the sensitivity and specificity of ROP screening. …”
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Synergistic coupling of ultrasonic cavitation with tailored deep eutectic solvent systems for intensified extraction of Scutellaria Radix flavonoids: Modelling and optimization by... by Qi Cui, Jia-Yi Shi, Le-Le Wen, Wei-Hua Kong, Li-Jie Jiang, Ju-Zhao Liu

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Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection by Ruo-Fei Xu, Zhen-Jing Liu, Shunan Ouyang, Qin Dong, Wen-Jing Yan, Dong-Wu Xu

“…We employed a two-stage machine learning approach: first applying Recursive Feature Elimination with multiple linear regression to identify core predictive items for total depression scores, followed by logistic regression for optimizing depression classification (CES-D ≥ 16). Model performance was systematically evaluated through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. …”
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Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI by Sihwan Kim, Changmin Park, Gwanghyeon Jeon, Seohee Kim, Jong Hyo Kim

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All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning by Min A, Liu Y, Fu M, Hou Z, Wang Z

“…Cox proportional hazards regression is used to explore the association between fractures type and mortality. Boruta algorithm was used to screen the risk factors related to death. …”
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Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm by Yushuai Yuan, Li Yang, Rui Gao, Cheng Chen, Min Li, Jun Tang, Xiaoyi Lv, Ziwei Yan

“…The samples were input into the SVM after division by the Kennard–Stone (KS) algorithm. Compared with other models, the SVM optimized by a grid search (GS) algorithm performed the best. …”
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Development and validation of a predictive model for new HIV infection screening among persons 15 years and above in primary healthcare settings in Kenya: a study protocol by Simon Karanja, Amos Otieno Olwendo, Gideon Kikuvi

“…Introduction This study seeks to determine incidence, comorbidities and drivers for new HIV infections to develop, test and validate a risk prediction model for screening for new cases of HIV.Methods and analysis The study has two components: a cross-sectional study to develop the prediction model using the HIV dataset from the Kenya AIDS and STI Control Programme and a 15-month prospective study for the validation of the model. …”
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Diagnostic accuracy of artificial intelligence models in detecting congenital heart disease in the second-trimester fetus through prenatal cardiac screening: a systematic review an... by Lies Dina Liastuti, Lies Dina Liastuti, Yosilia Nursakina, Yosilia Nursakina

“…Most studies utilized deep learning models using either ultrasound or echocardiographic images. …”
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Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera by Ashish Sharma, Taraprasad Das, Bryan Ong, Florian Mickael Savoy, Divya Parthasarathy Rao, Jun Kai Toh, Anand Sivaraman

“…The model was designed to indicate the presence of referable AMD (intermediate and advanced AMD). …”
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To Determine the Risk-Based Screening Interval for Diabetic Retinopathy: Development and Validation of Risk Algorithm from a Retrospective Cohort Study by Jinxiao Lian, Ching So, Sarah Morag McGhee, Thuan-quoc Thach, Cindy Lo Kuen Lam, Colman Siu Cheung Fung, Alfred Siu Kei Kwong, Jonathan Cheuk Hung Chan

“…Methods The retrospective cohort data from 117,418 subjects who received systematic DR screening in Hong Kong between 2010 and 2016 were included to develop and validate the risk algorithm using a parametric survival model. …”
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Prediction of pulmonary embolism by an explainable machine learning approach in the real world by Qiao Zhou, Ruichen Huang, Xingyu Xiong, Zongan Liang, Wei Zhang

“…To address this, we employed an artificial intelligence–based machine learning algorithm (MLA) to construct a robust predictive model for PE. …”
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Identification of immune-related biomarkers linked to systemic lupus erythematosus and dilated cardiomyopathy through integrated bioinformatics analysis and multiple machine learni... by Gaijie Li, Liwen Lin, Shushu Wang, Kachun Lu, KaMan Szeto, Guiting Zhou, Xianwen Tang, Chuanjin Luo, Chuanjin Luo

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Toward Next-Generation Biologically Plausible Single Neuron Modeling: An Evolutionary Dendritic Neuron Model by Chongyuan Wang, Huiyi Liu

“…Beyond technical performance, the proposed model supports societal applications requiring trustworthy AI, such as interpretable medical diagnostics, financial screening, and low-energy embedded systems. …”
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