Machine learning algorithms reveal the secrets of mitochondrial dynamics by Jack J Collier, Robert W Taylor

“…In this issue of EMBO Molecular Medicine, supervised machine learning algorithms underlie a novel tool that enables automated, high throughput and unbiased screening of changes in mitochondrial morphology measured using confocal microscopy. …”
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Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer by Haojie Dai, Zijie Yu, You Zhao, Ke Jiang, Zhenyu Hang, Xin Huang, Hongxiang Ma, Li Wang, Zihao Li, Ming Wu, Jun Fan, Weiping Luo, Chao Qin, Weiwen Zhou, Jun Nie

“…Subsequently by multivariate cox regression as well as survshap(t) model we screened core prognostic gene and identified it by Mendelian randomization. …”
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Exploring the association between vitamin D levels and dyslipidemia risk: insights from machine learning and generalized additive models by Yin Tianxiu, Zhang Chen, Liu Yuxiang, Zhu Xiaoyue, Hu Jingyao, Guo Haijian, Wang Bei

“…Subsequently, multiple logistic regression and a generalized additive model (GAM) were utilized to construct models analyzing the association between vitamin D levels and dyslipidemia.ResultsIn our study, the XGboost machine learning algorithm explored the relative importance of all included variables, confirming a robust association between vitamin D levels and dyslipidemia. …”
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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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Preliminary exploration and application research on the model of gathering distillate according to the quality based on Fourier transform near infrared spectroscopy by LIAO Li, ZHANG Guiyu, ZOU Yongfang, ZHU Xuemei, PENG Houbo, ZHANG Wei, LI Yan

“…The spectrum was obtained by Fourier transform near-infrared spectroscopy (FT-NIR), and the spectrum pretreatment and wavelength screening were performed, the regression prediction model was established based on the principal components, and the model of gathering distillate according to the quality was constructed by random forest (RF). …”
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Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage by Weizhi Qiu, Penglei Cui, Shaojie Li, Zhenzhou Tang, Jiani Chen, Jiayin Wang, Yasong Li

“…Five machine learning algorithms were used to construct the prediction model and the model accuracy was evaluated by ROC curves. …”
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Progress and current trends in prediction models for the occurrence and prognosis of cancer and cancer-related complications: a bibliometric and visualization analysis by Siyu Li, Wenrui Li, Xiaoxiao Wang, Wanyi Chen

“…Emerging modeling techniques, such as neural networks and deep learning algorithms, are likely to play a pivotal role in current and future cancer-related prediction model research. …”
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Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability by Deyan Liu, Yuge Tian, Min Liu, Shangjian Yang

“…LASSO regression, combined with univariable and multivariable logistic regression, was employed to select feature variables for predictive modeling. Seven machine learning algorithms, including logistic regression, decision tree, random forest, support vector machine, gradient boosting decision tree, k-nearest neighbors, and neural network, were used to develop predictive models. …”
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Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study by Rong Wu, Yu Zhang, Peijie Huang, Yiying Xie, Jianxun Wang, Shuangyong Wang, Qiuxia Lin, Yichen Bai, Songfu Feng, Nian Cai, Xiaohe Lu

“…ObjectiveTo develop and validate prediction models for reactivation after anti-VEGF intravitreal injection in infants with ROP using multimodal machine learning algorithms. …”
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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

“…The IoT-based architecture enables seamless integration with cloud computing platforms, allowing remote access and scalability for large-scale population-level screening and monitoring. The system captures glucose-related optical signals, which are analyzed using various machine learning algorithms, including a novel Convolutional Neural Network–Attention Hybrid Model (CNN-AHM). …”
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Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning by Su Zhang, Weitao Hu, Yuchao Tang, Hongjie Lin, Xiaoqing Chen

“…Three machine learning algorithms were applied to DE-IRGs to screen for hub DE-IRGs. …”
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Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground‐Glass Nodules Based on AI‐Extracted Radiomic Features by Guozhen Yang, Yuanheng Huang, Huiguo Chen, Weibin Wu, Yonghui Wu, Kai Zhang, Xiaojun Li, Jiannan Xu, Jian Zhang

“…This study aimed to develop a machine learning (ML)–based model using artificial intelligence (AI)‐extracted CT radiomic features to predict the invasiveness of GGNs. …”
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Development and Clinical Validation of Visual Inspection With Acetic Acid Application-Artificial Intelligence Tool Using Cervical Images in Screen-and-Treat Visual Screening for Ce... by Usha Rani Poli, Anirudh G. Gudlavalleti, Jaya Bharadwaj Y, Hira B. Pant, Varun Agiwal, G.V.S. Murthy

“…The perceived challenge rate for false positives was 20%.CONCLUSIONThis novel cervical image–based VIA-AI algorithm showed promising results in real-life settings, and could help minimize overtreatment in single-visit VIA screening and treatment programs in resource-constrained situations.…”
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In-Silico discovery of novel cephalosporin antibiotic conformers via ligand-based pharmacophore modelling and de novo molecular design by Rayhan Chowdhury, Samia Akter Saima, Md. Al Amin, Md. Kawsar Habib, Ramisa Binti Mohiuddin, Ali Mohamod Wasaf Hasan, Roksana Khanam, Shahin Mahmud

“…The generated pharmacophore model, with a score of 0.9268, was utilized to screen a drug library, initially assessing 19 compounds. …”
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Development of a predictive model for risk factors of multidrug-resistant bacterial pneumonia in critically ill post-neurosurgical patients by Aixiang Hu, Dayan Ma, Yanni Lei, Fangqiang Li, Xi Wang, Yuewei Zhang

“…However, existing prediction frameworks exhibit limitations in elucidating the relative importance of risk factors, thereby impeding precise clinical decision-making and individualized patient management.ObjectiveTo evaluate the performance of six ensemble classification algorithms and three single classification algorithms in predicting MDR-BP risk factors among neurosurgical postoperative critically ill patients, identify the optimal predictive model, and determine key influential factors.MethodsWe conducted a retrospective study involving 750 neurosurgical patients admitted to a neurosurgery center at a tertiary hospital in Beijing between January 2020 and December 2023. …”
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Proteomic signatures and predictive modeling of cadmium-associated anxiety in middle-aged and elderly populations: an environmental exposure association study by Sheng Wan, Yong Yang, Qihan Zhao, Zelong Xing, Jie Li, Hao Gao, Yinghui Yin, Zhenzhong Liu, Qiwen Chen, Maoqin Tian, Xinxin Shi, Ziyue Ji, Shaoxin Huang

“…Machine learning techniques, specifically XGBoost and LASSO, were employed to identify biomarkers that were subsequently validated through mediation analysis and animal experiments, allowing for the screening of key protein signatures. Finally, clinical variables were integrated to construct a comprehensive model, which was then thoroughly evaluated. …”
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Predicting the risk of postoperative gastrointestinal bleeding in patients with Type A aortic dissection based on an interpretable machine learning model by Lin Li, Xing Yang, Wei Guo, Wenxian Wu, Meixia Guo, Huanhuan Li, Xueyan Wang, Siyu Che

“…Predictors were screened using LASSO regression, and four ML algorithms—Random Forest (RF), K-nearest neighbor (KNN), Support Vector Machines (SVM), and Decision Tree (DT)—were employed to construct models for predicting postoperative GIB risk. …”
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Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models by Yalei Wang, Yuqing Xin, Baoqi Zhang, Fuqiang Pan, Xu Li, Manman Zhang, Yushan Yuan, Lei Zhang, Peiqi Ma, Bo Guan, Yang Zhang

“…Models were constructed using the LightGBM algorithm: a radiomic feature model, a deep learning feature model, and a combined model integrating radiomic and deep learning features. …”
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Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis by Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Behnaz Niroomand, Nasira Faizi, Pooya Eini, Mohammad Amin Habibi, Alireza Mohseni, Mohammadmahdi Sabahi, Abdulrahman Albakr, Michael Karsy, Hamid Borghei-Razavi

“…For the comparison between Logistic Regression (LR) and non-LR algorithms, LR-based algorithms exhibited numerically higher AUC and sensitivity; however, these differences were not statistically significant. …”
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Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study by Yimei Zhang, Yu Wang, Jingran Yang, Qinglan Li, Min Zhou, Jiafei Lu, Qiulan Hu, Fang Ma

“…Eighteen features screened through a previous umbrella review informed the models. …”
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