Мethods of Machine Learning in Ophthalmology: Review by D. D. Garri, S. V. Saakyan, I. P. Khoroshilova-Maslova, A. Yu. Tsygankov, O. I. Nikitin, G. Yu. Tarasov

“…Machine learning includes models and algorithms that mimic the architecture of biological neural networks. …”
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Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques by Liuqing Yang, Liuqing Yang, Liuqing Yang, Rui Xuan, Rui Xuan, Rui Xuan, Dawei Xu, Dawei Xu, Dawei Xu, Aming Sang, Aming Sang, Aming Sang, Jing Zhang, Jing Zhang, Jing Zhang, Yanfang Zhang, Xujun Ye, Xinyi Li, Xinyi Li, Xinyi Li

“…The utilization of the receiver operating characteristic curve in conjunction with the nomogram model served to authenticate the discriminatory strength and efficacy of the key genes. …”
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Collaborative Filtering Techniques for Predicting Web Service QoS Values in Static and Dynamic Environments: A Systematic and Thorough Analysis by Ghizlane Khababa, Sadik Bessou, Fateh Seghir, Nor Hazlyna Harun, Abdulaziz S. Almazyad, Pradeep Jangir, Ali Wagdy Mohamed

“…Key insights were gathered on algorithms, evaluation metrics, datasets, and performance outcomes, with a focus on CF methods and advancements in hybrid and context-aware models. …”
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High-flow nasal cannula therapy versus continuous positive airway pressure for non-invasive respiratory support in paediatric critical care: the FIRST-ABC RCTs by Padmanabhan Ramnarayan, Alvin Richards-Belle, Karen Thomas, Laura Drikite, Zia Sadique, Silvia Moler Zapata, Robert Darnell, Carly Au, Peter J Davis, Izabella Orzechowska, Julie Lester, Kevin Morris, Millie Parke, Mark Peters, Sam Peters, Michelle Saull, Lyvonne Tume, Richard G Feltbower, Richard Grieve, Paul R Mouncey, David Harrison, Kathryn Rowan

“…More patients switched from CPAP to HFNC (30.9%) than from HFNC to CPAP (20.0%). …”
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Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence by Marco Zeppieri, Lorenzo Gardini, Carola Culiersi, Luigi Fontana, Mutali Musa, Fabiana D’Esposito, Pier Luigi Surico, Caterina Gagliano, Francesco Saverio Sorrentino

“…By automating standard screening procedures, these models have demonstrated promise in distinguishing between glaucomatous and healthy eyes, forecasting the course of the disease, and possibly lessening the workload of physicians. …”
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Ferroptosis-related hub genes and immune cell dynamics as diagnostic biomarkers in age-related macular degeneration by Jinquan Chen, Zhao Long, Dandan Shi, Qian Zhang, H. Peng

“…Consequently, the macular was selected as the primary focus of the study. Subsequent screening of these 19 genes using LASSO regression, Support Vector Machine (SVM), and Random Forest algorithms identified four hub genes: FADS1, TFAP2A, AKR1C3, and TTPA. …”
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Color and Grey-Level Co-Occurrence Matrix Analysis for Predicting Sensory and Biochemical Traits in Sweet Potato and Potato by Judith Ssali Nantongo, Edwin Serunkuma, Gabriela Burgos, Mariam Nakitto, Joseph Kitalikyawe, Thiago Mendes, Fabrice Davrieux, Reuben Ssali

“…With instrumental color and texture parameters as predictors, low to moderate accuracy was detected in the machine learning models developed to predict sensory panel traits. …”
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Uncovering Hippo pathway-related biomarkers in acute myocardial infarction via scRNA-seq binding transcriptomics by Xingda Li, Xueqi He, Yu Zhang, Xinyuan Hao, Anqi Xiong, Jiayu Huang, Biying Jiang, Zaiyu Tong, Haiyan Huang, Lian Yi, Wenjia Chen

“…Three machine-learning algorithms prioritized five biomarkers (NAMPT, CXCL1, CREM, GIMAP6, and GIMAP7), validated through multi-dataset analyses and cellular expression profiling. qRT-PCR and Western blot confirmed differential expression patterns between AMI and controls across experimental models. …”
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Transforming heart transplantation care with multi-omics insights by Zhengbang Zou, Jianing Han, Zhiyuan Zhu, Shanshan Zheng, Xinhe Xu, Sheng Liu

“…Single–cell omics technologies and machine learning algorithms further resolve cellular heterogeneity and improve predictive modeling, thereby enhancing the clinical translatability of multi-omics data. …”
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Association of urinary metal elements with sarcopenia and glucose metabolism abnormalities: Insights from NHANES data using machine learning approaches by Xinmin Jin, Lei Li, Xiaoyan Hu, Pengfei Bi, Song Zhang, Qian Wang, Zhongwei Xiao, Hua Yang, Tongtong Liu, Lifang Feng, Jinhuan Wang

“…Objectives: This study aimed to explore the association between urinary metal element levels and sarcopenia across different glucose metabolic states using multi-omics clustering algorithms and machine learning models, and to identify diagnostic biomarkers. …”
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The microRNA expression signature of CD4+ T cells in the transition of brucellosis into chronicity. by Ferah Budak, Salih Haldun Bal, Gulcin Tezcan, Emin Halis Akalın, Abdullah Yılmaz, Pınar Hız, Haluk Barbaros Oral

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Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics by Yi Ding, Zhaiyue Xu, Wenjing Hu, Peng Deng, Mian Ma, Jiandong Wu

“…The eight-gene GloMICS score outperformed 95 published prognostic models (C-index 0.74–0.66 across TCGA, CGGA and GEO). …”
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Locating and quantifying CH₄ sources within a wastewater treatment plant based on mobile measurements by J. Yang, Z. Xu, Z. Xia, Z. Xia, X. Pei, Y. Yang, B. Qiu, B. Qiu, S. Zhao, S. Zhao, Y. Zhang, Y. Zhang, Z. Wang, Z. Wang

“…We utilized a multi-source Gaussian plume model combined with a genetic algorithm inversion framework, designed to locate major sources within the plant and quantify the corresponding <span class="inline-formula">CH₄</span> emission fluxes. …”
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Shared and Distinctive Inflammation-Related Protein Profiling in Idiopathic Inflammatory Myopathy with/without Anti-MDA5 Autoantibodies by Zhang Y, Hu W, Li T, Pan Z, Sun J, He Y, Guan W, Zhang L, Lian C, Liu S, Zhang P

“…The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to anti-MDA5+ DM.Results: Compared with HCs, 36 inflammation-related proteins were identified as DEPs. …”
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Purine metabolism-associated key genes depict the immune landscape in gout patients by Lin-na Li, Hao Wang, Lu-shan Xiao, Wei-nan Lai

“…Using RNA-seq data of peripheral blood mononuclear cells (PBMCs) from gout patients, we screened the differentially expressed genes (DEGs) of gout patients and found that they were closely involved in purine metabolism. …”
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Prostate cancer and metabolic syndrome: exploring shared signature genes through integrative analysis of bioinformatics and clinical data by Maomao Guo, Sudong Liang, Zhenghui Guan, Jingcheng Mao, Zhibin Xu, Wenchao Zhao, Hao Bian, Jianfeng Zhu, Jiangping Wang, Xin Jin, Yuan Xia

“…In this study, we utilized bioinformatics and machine learning techniques to analyze public datasets and validated our findings using clinical specimens from our center to identify common signature genes between PCa and MS. We began by screening differentially expressed genes (DEGs) and module genes through Linear models for microarray analysis (Limma) and Weighted Gene Co-expression Network Analysis (WGCNA) of four microarray datasets from the GEO database (PCa: GSE8511, GSE32571, and GSE104749; MS: GSE98895). …”
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Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus by Changfan Peng, Long Hu, Wanwen Su, Xin Hu

“…Concurrently, gene expression datasets from GEO (POAG: GSE27276; COVID-19: GSE171110, GSE152418) were used to identify 57 crosstalk genes (CGs) via differential expression analysis. Machine learning algorithms (LASSO, SVM-RFE, Random Forest) were applied to screen POAG diagnostic biomarkers from CGs, followed by construction of transcription factor (TF)-microRNA (miRNA)-protein-compound regulatory networks and consensus clustering to characterize COVID-19 immune microenvironment subtypes. …”
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The Role of AI in Nursing Education and Practice: Umbrella Review by Rabie Adel El Arab, Omayma Abdulaziz Al Moosa, Fuad H Abuadas, Joel Somerville

“…First, ethical and social implications were consistently highlighted, with studies emphasizing concerns about data privacy, algorithmic bias, transparency, accountability, and the necessity for equitable access to AI technologies. …”
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Combining Near-Infrared Spectroscopy and Chemometrics for Rapid Recognition of an Hg-Contaminated Plant by Bang-Cheng Tang, Hai-Yan Fu, Qiao-Bo Yin, Zeng-Yan Zhou, Wei Shi, Lu Xu, Yuan-Bin She

“…The NIRS measurements of impacted sample powders were collected in the mode of reflectance. The DUPLEX algorithm was utilized to split the NIRS data into representative training and test sets. …”
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Cer(d18:1/16:0) as a biomarkers for acute coronary syndrome in Chinese populations by Liang Zhang, Yang Zhang, YaoDong Ding, Tong Jin, Yi Song, Lin Li, XiaoFang Wang, Yong Zeng

“…The area under the ROC curve was used to screen the most valuable predictor. Distinctive ACS-related variables were screened out using Boruta and LASSO regression. …”
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