Analysis and Validation of Autophagy-Related Gene Biomarkers and Immune Cell Infiltration Characteristic in Bronchopulmonary Dysplasia by Integrating Bioinformatics and Machine Lea... by Xiao S, Ding Y, Du C, Lv Y, Yang S, Zheng Q, Wang Z, Zheng Q, Huang M, Xiao Q, Ren Z, Bi G, Yang J

“…Subsequently, the hub genes were identified by Lasso and Cytoscape with three machine-learning algorithms (MCC, Degree and MCODE). In addition, hub genes were validated with ROC, single-cell sequence and IHC in hyperoxia mice. …”
Get full text

Elucidating the dynamic tumor microenvironment through deep transcriptomic analysis and therapeutic implication of MRE11 expression patterns in hepatocellular carcinoma by Ruiqiu Chen, Chaohui Xiao, Zizheng Wang, Guineng Zeng, Shaoming Song, Gong Zhang, Lin Zhu, Penghui Yang, Rong Liu

“…Publicly available single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics were utilized to explore MRE11’s dynamic mechanisms in the tumor microenvironment (TME) of both primary and post-immunotherapy cases. We also screened for differentially expressed genes and constructed a robust HCC prognosis model using 101 machine-learning algorithms. …”
Get full text

Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis by Dongxu Qin, Yongquan Zheng, Libo Wang, Zhenyi Lin, Yao Yao, Weidong Fei, Caihong Zheng

“…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify key genes. Machine learning algorithms, including Random Forest (RF) and XGBoost, were utilized to screen for shared diagnostic genes, which were subsequently validated through receiver operating characteristic (ROC) analysis and clinical prediction models. …”
Get full text

Identification and verification of biomarkers associated with neutrophils in acute myocardial infarction: integrated analysis of bulk RNA-seq, expression quantitative trait loci, a... by Guoqing Liu, Xiangwen Lv, Jiahui Qin, Xingqing Long, Miaomiao Zhu, Chuwen Fu, Jian Xie, Peichun He

“…Hub genes were screened using the least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms. …”
Get full text

A Non-Uniformity Correction Method for Uncooled Infrared Polarization Imaging Systems by Cailing Zhao, Zhiguo Fan, Yunxiang Zhang

“…Previous non-uniformity correction (NUC) algorithms usually couple polarization information with FPN correction, resulting in the loss of polarization characteristics. …”
Get full text

Improving synergistic drug combination prediction with signature-based gene expression features in oncology by Mozhgan Mozaffarilegha, Sajjad Gharaghani

“…We compared their performance with that of conventional drug signatures and chemical structure-based descriptors.Results:Our results demonstrate that models incorporating DRS features consistently outperform traditional approaches across all evaluated algorithms. …”
Get full text

Meta-analyses of IL1A polymorphisms and the risk of several autoimmune diseases published in databases. by Hang Su, Na Rei, Lei Zhang, Jiaxiang Cheng

Get full text

Identification of the immune infiltration and biomarkers in ulcerative colitis based on liquid–liquid phase separation-related genes by Zhixing Hong, Shilin Fang, Haihang Nie, Jingkai Zhou, Yuntian Hong, Lan Liu, Qiu Zhao

“…We identified the hub LLPS-RGs (DE-LLPS-RGs) (HSPB3, SLC16A1, TRIM22, SRI, PLEKHG6, GBP1, PADI2) by machine learning algorithms. Hub genes were screened that displayed high prediction accuracy of UC patients. …”
Get full text

Key factors determination of hyperuricemia and association analysis among patients with breast cancer: results from NHANES data by Ting-ting Meng, Wen-rui Wang, Yan-qing Zheng, Guan-dong Liu

“…ObjectivesTo explore the factors influencing hyperuricemia in breast cancer patients based on the National Health and Nutrition Examination Survey (NHANES) database.MethodsThe univariate and multivariate generalized linear regression were used to screen the influencing factors of hyperuricemia. Logistic and XGBoost algorithms were used to rank the importance of influencing factors. …”
Get full text

Epidemiology and Association Rules Analysis for Pulmonary Tuberculosis Cases with Extrapulmonary Tuberculosis from Age and Gender Perspective: A Large-Scale Retrospective Multicent... by Wanli Kang, Jiajia Yu, Chen Liang, Quanhong Wang, Liang Li, Jian Du, Hongyan Chen, Jianxiong Liu, Jinshan Ma, Mingwu Li, Jingmin Qin, Wei Shu, Peilan Zong, Yi Zhang, Xiaofeng Yan, Zhiyi Yang, Zaoxian Mei, Qunyi Deng, Pu Wang, Wenge Han, Meiying Wu, Ling Chen, Xinguo Zhao, Lei Tan, Fujian Li, Chao Zheng, Hongwei Liu, Xinjie Li, Ertai A., Yingrong Du, Fenglin Liu, Wenyu Cui, Song Yang, Xiaohong Chen, Junfeng Han, Qingyao Xie, Yanmei Feng, Wenyu Liu, Peijun Tang, Jianyong Zhang, Jian Zheng, Dawei Chen, Xiangyang Yao, Tong Ren, Yan Li, Yuanyuan Li, Lei Wu, Qiang Song, Mei Yang, Jian Zhang, Yuanyuan Liu, Shuliang Guo, Kun Yan, Xinghua Shen, Dan Lei, Yanli Zhang, Youcai Li, Yongkang Dong, Shenjie Tang

“…The multivariable logistic regression models demonstrated that odds ratios of concurrent PTB-EPTB cases varied by gender and age group. …”
Get full text

Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation by Guiling Wu, Guiling Wu, Sihui Wu, Sihui Wu, Tian Xiong, Tian Xiong, Tian Xiong, You Yao, You Yao, Yu Qiu, Yu Qiu, Yu Qiu, Liheng Meng, Cuihong Chen, Xi Yang, Xi Yang, Xi Yang, Xinghuan Liang, Yingfen Qin

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”
Get full text

Comprehensive Analysis of Programmed Cell Death-Related Genes in Diagnosis and Synovitis During Osteoarthritis Development: Based on Bulk and Single-Cell RNA Sequencing Data by Zhou J, Jiao S, Huang J, Dai T, Xu Y, Xia D, Feng Z, Chen J, Li Z, Hu L, Meng Q

“…Using machine learning algorithms, Hub PCD-related differentially expressed genes (Hub PCD-DEGs) were identified. …”
Get full text

Identification of Ferroptosis‐Related Gene in Age‐Related Macular Degeneration Using Machine Learning by Meijiang Zhu, Jing Yu

“…Differentially expressed genes (DEGs) were selected and intersected with genes from the ferroptosis database to obtain differentially expressed ferroptosis‐associated genes (DEFGs). Machine learning algorithms were employed to screen diagnostic genes. …”
Get full text

Identification of Serum miRNAs as Effective Diagnostic Biomarkers for Distinguishing Primary Central Nervous System Lymphoma from Glioma by Pei-pei Si, Xiao-hui Zhou, Zhen-zhen Qu

“…Candidate miRNAs were identified through SVM-RFE analysis and LASSO model. ROC assays were operated to determine the diagnostic value of serum miRNAs in distinguishing PCNSL from glioma. …”
Get full text

Mapping the digital silk road: evolution and strategic shifts in Chinese social media marketing (2015–2025) by Xinrui Liang, Wan Mohd Hirwani Wan Hussain, Mohammed R. M. Salem

“…Following Arksey and O’Malley’s five-stage scoping framework, 3,710 records from Web of Science and Scopus were screened, yielding 41 peer-reviewed studies. Results indicate a transition from search-based behaviour to AI-facilitated impulse purchasing, enabled by algorithmic recommendations, parasocial influencer relations, and livestream commerce. …”
Get full text

Mechanism and relevance of necroptosis to immune microenvironment of periodontitis: A pilot study by ZHENG Zhanglong, LI Jia, JIANG Jirui, SHAN Zhengnan, LI Shengjiao

“…[Objective:] To explore the effect and mechanism of necroptosis on the immune microenvironment of periodontitis. [Methods:] We screened out the differentially expressed necroptosis-related genes in periodontitis, first calculated the hub genes through machine learning algorithms, and constructed a diagnostic model. …”
Get full text

The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy by Szandra László, Ádám Nagy, József Dombi, Emőke Adrienn Hompoth, Emese Rudics, Zoltán Szabó, András Dér, András Búzás, Zsolt János Viharos, Anh Tuan Hoang, Vilmos Bilicki, István Szendi

“…By applying model-explaining tools to the well-performing models, we could conclude the movement patterns and characteristics of the groups. …”
Get full text

Deciphering the role of cuproptosis in the development of intimal hyperplasia in rat carotid arteries using single cell analysis and machine learning techniques by Miao He, Hui Chen, Zhengli Liu, Boxiang Zhao, Xu He, Qiujin Mao, Jianping Gu, Jie Kong

“…Methods: We downloaded single-cell sequencing and bulk transcriptome data from the GEO database to screen for copper-growth-associated genes (CAGs) using machine-learning algorithms, including Random Forest and Support Vector Machine. …”
Get full text

RNA m7G methylation regulators and targets significantly contribute to chronic obstructive pulmonary disease by Chenyu Zhu, Luyi Tan, Xinyu Zhang, Wenli Cheng, Min Li, Yibo Chen, Wenjuan Zhang

“…In this study, the combined roles of m7G methylation regulators were explored in COPD for the first time by integrated bioinformatic methods. The machine algorithms screened 7 disease signature genes relevant to clinical indicators, including CYFIP2, EIF3D, EIF4G3, GEMIN5, METTL1, SNUPN and NCBP2, and METTL1 was related to the progression in COPD. …”
Get full text

Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches by Jiayi Zhang, Zhixiang Jia, Jiahui Zhang, Xiaohui Mu, Limei Ai

“…Using the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms, we screened for seven potential diagnostic biomarkers with strong diagnostic capabilities: SMAD3, IL7R, IL18, FAS, CD5, CCR7, and CSF1R. …”
Get full text