Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review by Émile Lemoine, Joel Neves Briard, Bastien Rioux, Oumayma Gharbi, Renata Podbielski, Bénédicte Nauche, Denahin Toffa, Mark Keezer, Frédéric Lesage, Dang K. Nguyen, Elie Bou Assi

“…The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. …”
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Identification of aging-related biomarkers and immune infiltration analysis in renal stones by integrated bioinformatics analysis by Yuanzhao Wang, Nana Chen, Bangqiu Zhang, Pingping Zhuang, Bingtao Tan, Changlong Cai, Niancai He, Hao Nie, Songtao Xiang, Chiwei Chen

“…Using logistic regression, SVM, and LASSO regression algorithms, a successful early-diagnosis model for RS was developed, yielding 7 key genes: CNR1, KIT, HTR2A, DES, IL33, UCP2, and PPT1. …”
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Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning by Haoran Xie, Junjun Wang, Qiuyan Zhao

“…Protein-Protein Interaction (PPI) network and machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF), were applied to screen for signature MRDEGs. …”
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Shared pathogenic mechanisms linking obesity and idiopathic pulmonary fibrosis revealed by bioinformatics and in vivo validation by Linjie Chen, Haojie Chen, Zinan Chen, Kunyi Zhang, Hongsen Zhang, Jiahe Xu, Tongsheng Chen

“…Functional enrichment (GO/KEGG), protein-protein interaction (PPI) networks, and machine learning algorithms were applied to screen hub genes, validated by ROC curves. …”
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Prediction and validation of anoikis-related genes in neuropathic pain using machine learning. by Yufeng He, Ye Wei, Yongxin Wang, Chunyan Ling, Xiang Qi, Siyu Geng, Yingtong Meng, Hao Deng, Qisong Zhang, Xiaoling Qin, Guanghui Chen

“…We also used rats to construct an NP model and validated the analyzed hub genes using hematoxylin and eosin (H&E) staining, real-time polymerase chain reaction (PCR), and Western blotting assays.…”
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Prognostic, oncogenic roles, and pharmacogenomic features of AMD1 in hepatocellular carcinoma by Youliang Zhou, Yi Zhou, Jiabin Hu, Yao Xiao, Yan Zhou, Liping Yu

“…Univariate Cox regression analysis and Pearson correlation were used to screen for AMD1-related genes (ARGs). Multidimensional bioinformatic algorithms were utilized to establish a risk score model for ARGs. …”
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Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning. by Yinghao Ren, Weiqiang Chen, Yuhao Lin, Zeyu Wang, Weiliang Wang

“…Furthermore, we utilized least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF) algorithms to screen for hub genes. We then validated the expression of these hub genes and constructed nomograms for further validation. …”
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Multi-Omics Identification of <i>Fos</i> as a Central Regulator in Skeletal Muscle Adaptation to Long-Term Aerobic Exercise by Chaoyang Li, Xinyuan Zhu, Yi Yan

“…Key feature genes were screened using Lasso regression, SVM-RFE, and Random Forest machine learning algorithms, validated by RT-qPCR, and refined through PPI network analysis. …”
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DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure by Wenwu Tang, Wenwu Tang, Zhixin Wang, Xinzhu Yuan, Liping Chen, Haiyang Guo, Zhirui Qi, Ying Zhang, Xisheng Xie

“…In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.ResultsTotally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. …”
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Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis by Shifen Wang, Hong Tao, Xingyun Zhao, Siwen Wu, Chunwei Yang, Yuanfei Shi, Zhenshu Xu, Dawei Cui

“…Moreover, four hub genes (CXCL9, CCL18, C1QA and CTSC) were significantly screened from the three datasets using RF algorithms. …”
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Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach by Huali Jiang, Weijie Chen, Benfa Chen, Tao Feng, Heng Li, Dan Li, Shanhua Wang, Weijie Li

“…Machine learning algorithms (Support Vector Machine (SVM), Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO)) were applied to identify hub genes. …”
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Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review by Ethan E Abbott, Donald Apakama, Lynne D Richardson, Lili Chan, Girish N Nadkarni

“…With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. …”
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MSGEGA: Multiscale Gaussian Enhancement and Global-Aware Network for Infrared Small Target Detection by Yuyang Xi, Liuwei Zhang, Ying Jiang, Feng Qian, Fanjiao Tan, Qingyu Hou

“…Specifically, the proposed method demonstrates significant advantages on the screened dataset, achieving an AUC of 0.992. At a detection rate of 0.871, it maintains a false alarm rate of 0.9<italic>e</italic>-5, outperforming all comparison algorithms. …”
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Comparative assessment of line probe assays and targeted next-generation sequencing in drug-resistant tuberculosis diagnosisResearch in context by Giovanna Carpi, Marva Seifert, Andres De la Rossa, Swapna Uplekar, Camilla Rodrigues, Nestani Tukvadze, Shaheed V. Omar, Anita Suresh, Timothy C. Rodwell, Rebecca E. Colman

“…Interpretation: LPAs demonstrated lower sensitivity and more limited drug resistance detection compared to tNGS workflows, underscoring the advantages of tNGS for improving DR-TB diagnostic algorithms. …”
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Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma by Zhen Chen, Yongjun Zhang

“…Considering the critical role of tumor infiltrating lymphocytes in effective immunotherapy, this study was designed to screen molecular markers related to tumor infiltrating cells in LUAD, aiming to improve immunotherapy response during LUAD therapy. …”
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Mesangial cell-derived CircRNAs in chronic glomerulonephritis: RNA sequencing and bioinformatics analysis by Ji Hui Fan, Xiao Min Li

“…Furthermore, three hub mRNAs (BOC, MLST8, and HMGCS2) from the CeRNA network were screened using LASSO algorithms. GSEA analysis revealed that hub mRNAs were implicated in a great deal of immune system responses and inflammatory pathways, including IL-5 production, MAPK signaling pathway, and JAK-STAT signaling pathway. …”
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A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER database by Fei Wang, Xihao Wang, Zhigang Feng, Jun Li, Hailiang Xu, Hengming Lu, Lianqu Wang, Zhihui Li

“…Potential risk factors were initially screened applying the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms. …”
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Deciphering mitochondrial dysfunction in keratoconus: Insights into ACSL4 from machine learning-based bulk and single-cell transcriptome analyses and experimental validation by Yuchen Cai, Tianyi Zhou, Xueyao Cai, Wenjun Shi, Hao Sun, Yao Fu

“…Hub genes were further screened and validated by multiple machine learning (ML) algorithms, followed by a comprehensive visualization of single-cell atlas and immune landscape. …”
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Identification and mechanism analysis of biomarkers related to butyrate metabolism in COVID-19 patients by Wenchao Zhou, Hui Li, Juan Zhang, Changsheng Liu, Dan Liu, Xupeng Chen, Jing Ouyang, Tian Zeng, Shuang Peng, Fan Ouyang, Yunzhu Long, Yukun Li

“…Six machine learning algorithms were employed to determine the best model for identifying biomarkers, and receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic value of the biomarkers in COVID-19. …”
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Exploring Mechanisms of Lang Qing Ata in Non-Alcoholic Steatohepatitis Based on Metabolomics, Network Pharmacological Analysis, and Experimental Validation by Li S, Zhu H, Zhai Q, Hou Y, Yang Y, Lan H, Jiang M, Xuan J

“…These discoveries were further validated in subsequent mouse models. An HFHC-induced NASH mouse model was used to validate the therapeutic effects and potential mechanisms of LQAtta on NASH.Results: From the UHPLC-MS/MS analysis of LQAtta, a total of 1518 chemical components were identified, with 106 of them being absorbed into the bloodstream. …”
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