Identification of a Prognosis-Related Risk Signature for Bladder Cancer to Predict Survival and Immune Landscapes by Linhui Wang, Yutao Wang, Jianfeng Wang, Luanfeng Li, Jianbin Bi

“…The Kaplan–Meier survival curves and receiver operating characteristics were used to evaluate the model’s effectiveness. The potential biological functions of the selected genes were analyzed using CIBERSORT and ESTIMATE algorithms. …”
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Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography by Jiao Wu, YongXin Li, Wanqing Gong, Qian Li, Xue Han, Tingting Zhang

“…Abstract Objective To develop a multi-modality machine learning-based radiomics model utilizing Magnetic Resonance Imaging (MRI), Ultrasound (US), and Mammography (MMG) for the differentiation of benign and malignant breast nodules. …”
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An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes by Na An, Zhongwen Lu, Yang Li, Bing Yang, Shaozhen Ji, Xu Dong, Zhaoliang Ding

“…Next, we identified differentially expressed genes (DEGs) that are associated with UC. …”
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Multi‐Omic Analysis Reveals a Lipid Metabolism Gene Signature and Predicts Prognosis and Chemotherapy Response in Thyroid Carcinoma by Yuqin Tu, Yanchen Chen, Linlong Mo, Guiling Yan, Jingling Xie, Xinyao Ji, Shu Chen, Changchun Niu, Pu Liao

“…The immune landscape was evaluated using the CIBERSORT algorithm, and chemotherapeutic response was predicted utilizing the “pRRophetic” R package. …”
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Identification and validation of aging related genes in osteoarthritis by Jian Du, Jian Du, Tian Zhou, Yanghui Dong, Yunchao Sun, Wei Peng

“…This study explores the relationship between aging related genes (ARGs) and OA, providing potential new targets for understanding the pathogenesis and treatment of OA.MethodsThe OA synovial tissue dataset was obtained from the GEO database, and differentially expressed genes (DEGs) were screened. The DEGs were intersected with ARGs to identify differentially expressed aging related genes (DEARGs), which were then subjected to functional enrichment analysis, PPI network analysis, and machine learning algorithms (LASSO and RF) to identify key genes. …”
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A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study by Lingfeng Zhang, Gang Xie, Yue Zhang, Yue Zhang, Junlin Li, Junlin Li, Wuli Tang, Wuli Tang, Ling Yang, Ling Yang, Kang Li

“…Ultimately, the efficacy of these models was evaluated by measuring the operating characteristics of the subjects through their area under the curve (AUCs).ResultsLogistic regression (LR) was found to be the most effective machine learning algorithm, for forecasting the MCE. …”
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Robust Vessel Segmentation in Fundus Images by A. Budai, R. Bock, A. Maier, J. Hornegger, G. Michelson

“…The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. …”
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Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis by Yang M, Zhang X, Zhou C, Du Y, Zhou M, Zhang W

“…Least Absolute Shrinkage and Selection Operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen hub genes. Immune cell infiltration was evaluated using CIBERSORT. …”
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Exploration of Epigenetic Mechanisms and Biomarkers Among Patients with Very-Late-Onset Schizophrenia-Like Psychosis by Gan Y, Yue W, Sun J, Yang D, Fang C, Zhou Z, Yin J, Zhou H

“…We prioritized key methylation sites through integrated analysis of methylation quantitative trait loci (meQTL), linkage disequilibrium (LD) patterns, and blood-brain methylation correlations. Machine learning algorithms generated diagnostic models, with classification performance evaluated using Area Under the Curve (AUC) metrics.Results: Analysis revealed distinct DNA methylation signatures in VLOSLP patients compared to controls. …”
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Integrated network toxicology and bioinformatics reveal PFAS-driven prognostic biomarkers and molecular mechanisms in breast cancer: insights from transcriptome analysis and molecu... by Yingqiang Fu, Yiyang Liu, Ziqi Sui

“…Enrichment analysis, network construction, and survival analysis were performed to elucidate the biological mechanisms and prognostic value. The CIBERSORT algorithm assessed immune cell infiltration, and molecular docking evaluated interactions between PFAS compounds and key genes. …”
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TMSB4X is a regulator of inflammation-associated ferroptosis, and promotes the proliferation, migration and invasion of hepatocellular carcinoma cells by Linlin Tang, Yangli Jin, Jinxu Wang, Xiuyan Lu, Mengque Xu, Mingwei Xiang

“…Univariate Cox regression analysis was conducted to screen prognostic genes, and 10 machine learning algorithms were combined to find the optimal strategy to evaluate the prognosis of the patients based on the prognosis-related genes. …”
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Developing a Prediction Model for Real-Time Incident Detection Leveraging User-Oriented Participatory Sensing Data by Md Tufajjal Hossain, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic, Lazar Spasovic

“…Real crash data from the New Jersey Department of Transportation (NJDOT) and crowdsourced data from Waze were matched using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to differentiate true and false alerts. A binary logit model was constructed to reveal significant predictors such as time categories around peak hours, road type, report ratings, and crash type. …”
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Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics. by Shuang Liu, Xuanpeng Zhao, Qingyan Meng, Baoshan Li

“…Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. …”
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A global comparison of social media bot and human characteristics by Lynnette Hui Xian Ng, Kathleen M. Carley

“…Finally, we discuss three open challenges and future directions of the study of bots: Detect, to systematically identify these automated and potentially evolving bots; Differentiate, to evaluate the goodness of the bot in terms of their content postings and relationship interactions; Disrupt, to moderate the impact of malicious bots, while not unsettling human conversations.…”
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Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study by Zhaobin He, Shengbiao Yang, Jianqiang Cao, Huijie Gao, Cheng Peng

“…This study employed nine ML algorithms to construct predictive models. Subsequently, model performance was evaluated and compared using several metrics, including the area under the receiver operating characteristic curve (AUC). …”
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Integrated multiple machine learning and Mendelian randomization reveal LTF gene as a prognostic biomarker for nonspecific orbital inflammation by Zixuan Wu, Jinfeng Xu, Yuan Gao, Kang Tan, Xiaolei Yao, Qinghua Peng

“…To further investigate the correlation between LTF and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. …”
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CMSS1: A RNA binding protein with pivotal roles in non-small cell lung cancer progression and prognosis by Zhe Fan, Wanyu Liu, Zhiwei Gao, Youfa Liu, Hongyang Hai, Zhenyang Lv

“…The relationship between the CMSS1 gene and tumor-infiltrating immune cells was assessed using the ImmuCellAI algorithm. Additionally, a loss-of-function assay was performed to investigate the functional role of CMSS1 in NSCLC cells. …”
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Diagnosis of parotid gland tumors using a ternary classification model based on ultrasound radiomics by Xiaoling Liu, Weihan Xiao, Chen Yang, Zhihua Wang, Dong Tian, Gang Wang, Xiachuan Qin

“…Two-step LASSO-BNB and voting ensemble learning modeling algorithm with recursive feature elimination feature selection method (RFE-Voting) models were then applied for classification. …”
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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…These genes were then subjected to functional enrichment analysis, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA). The CIBERSORT algorithm was utilized to evaluate immune cell infiltration patterns. …”
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Neutrophil extracellular traps-related genes contribute to sepsis-associated acute kidney injury by Tang Shaoqun, Yu Xi, Wang Wei, Luo Yaru, Lei Shaoqing, Qiu Zhen, Yang Yanlin, Sun Qian, Xia Zhongyuan

“…The OS-related genes were obtained by weighted gene co-expression network analysis. Differentially expressed genes were screened by “limma” package in R. …”
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