Construction and validation of acetylation-related gene signatures for immune landscape analysis and prognostication risk prediction in luminal breast cancer by Mengdi Zhu, Jinna Lin, Haohan Liu, Jingru Wang, Nianqiu Liu, Yudong Li, Hongna Lai, Qianfeng Shi

“…Using Consensus Cluster Plus and the LASSO risk model, we screened 6 acetylation-related genes (KAT2B, TAF1L, CDC37, CCDC107, C17orf106, and ASPSCR1) and constructed a 6-gene risk model of luminal breast cancer. …”
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PDP1 related ferroptosis risk signature indicates distinct immune microenvironment and prognosis of breast cancer patients by Yufeng Wang, Huifen Dang, Gongjian Zhu, Yingxia Tian

“…LASSO Cox regression was utilized to screen genes to build a RiskScore model, and survival analysis were performed to investigate the reliability in BC prognosis. …”
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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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Transcription factor networks and novel immune biomarkers reveal key prognostic and therapeutic insights in ovarian cancer by Aiqin Zhao, Sufang Zhou, Xiaoyi Yang, Haiying Lu, Dan Zou, Xuan Zhang, Li Liu

“…To analyze the percentage of invading immune cells, the algorithms CIBERSORT, ESTIMATE, and xCell were used. …”
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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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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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Integrative multi-omics analysis reveals the role of toll-like receptor signaling in pancreatic cancer by Jie Peng, Jiaao Sun, Youfeng Yu, Qihang Yuan, Yong Zhang

“…In the process of building prognostic models, we screened 33 core genes related to the prognosis of pancreatic cancer, and combined a series of machine learning algorithms to build the prognosis model of pancreatic cancer. …”
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Machine Learning–Based Prediction of Early Complications Following Surgery for Intestinal Obstruction: Multicenter Retrospective Study by Pinjie Huang, Jirong Yang, Dizhou Zhao, Taojia Ran, Yuheng Luo, Dong Yang, Xueqin Zheng, Shaoli Zhou, Chaojin Chen

“…ConclusionsWe have developed and validated a generalizable random forest model to predict postoperative early complications in patients undergoing intestinal obstruction surgery, enabling clinicians to screen high-risk patients and implement early individualized interventions. …”
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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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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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On the construction of a large-scale database of AI-assisted annotating lung ventilation-perfusion scintigraphy for pulmonary embolism (VQ4PEDB) by Amir Jabbarpour, Eric Moulton, Eric Moulton, Sanaz Kaviani, Sanaz Kaviani, Siraj Ghassel, Wanzhen Zeng, Wanzhen Zeng, Ramin Akbarian, Ramin Akbarian, Anne Couture, Aubert Roy, Richard Liu, Yousif A. Lucinian, Nuha Hejji, Nuha Hejji, Sukainah AlSulaiman, Sukainah AlSulaiman, Farnaz Shirazi, Farnaz Shirazi, Eugene Leung, Eugene Leung, Sierra Bonsall, Samir Arfin, Bruce G. Gray, Ran Klein, Ran Klein, Ran Klein, Ran Klein

“…The annotated data was then ingested into Deep Lake, a SQL-based database, for AI model training. Quality assurance involved manual inspections and algorithmic validation.ResultsA query of The Ottawa Hospital's data warehouse followed by initial data screening yielded 2,137 V/Q studies with 2,238 successfully retrieved as DICOM studies. …”
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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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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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Lessons from the PROTECT-CH COVID-19 platform trial in care homes by Philip M Bath, Jonathan Ball, Matthew Boyd, Heather Gage, Matthew Glover, Maureen Godfrey, Bruce Guthrie, Jonathan Hewitt, Robert Howard, Thomas Jaki, Edmund Juszczak, Daniel Lasserson, Paul Leighton, Val Leyland, Wei Shen Lim, Pip Logan, Garry Meakin, Alan Montgomery, Reuben Ogollah, Peter Passmore, Philip Quinlan, Caroline Rick, Simon Royal, Susan D Shenkin, Clare Upton, Adam L Gordon

“…Results We initiated the trial including protocol, approvals, insurance, website, database, data algorithms, intervention selection and training materials. …”
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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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Focusing on scRNA-seq-Derived T Cell-Associated Genes to Identify Prognostic Signature and Immune Microenvironment Status in Low-Grade Glioma by Jiayu Wen, Qiaoyi Huang, Jiuxiu Yao, Wei Wei, Zehui Li, Huiqin Zhang, Surui Chang, Hui Pei, Yu Cao, Hao Li

“…In addition, bulk RNA data of 975 LGG samples were collected for model construction. Algorithms such as TIMER, CIBERSORT, QUANTISEQ, MCPCOUTER, XCELL, and EPIC were used to depict the tumor microenvironment landscape. …”
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Automated machine learning for predicting perioperative ischemia stroke in endovascularly treated ruptured intracranial aneurysm patients by Yuhang Peng, Ke Bi, Xiaolin Zhang, Ning Huang, Xiang Ji, Weifu Chen, Ying Ma, Yuan Cheng, Yongxiang Jiang, Jianhe Yue

“…The least absolute shrinkage and selection operator (LASSO) method was used to screen essential features associated with PIS. Based on these features, nine machine learning models were constructed using a training set (75% of participants) and assessed on a test set (25% of participants). …”
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