Multi-Omics and Experimental Validation Identify GPX7 and Glutathione-Associated Oxidative Stress as Potential Biomarkers in Ischemic Stroke by Tianzhi Li, Sijie Zhang, Jinshan He, Hongyan Li, Jingsong Kang

“…Multidimensional feature screening using unsupervised consensus clustering and a series of machine learning algorithms led to the identification of the signature gene <i>GPX7</i>. …”
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Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning by V. Vigna, T. F. G. G. Cova, A. A. C. C. Pais, E. Sicilia

“…The model is efficient, fast, and resource-light, using decision tree-based algorithms that provide interpretable results. …”
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The impact of a coach-guided personalized depression risk communication program on the risk of major depressive episode: study protocol for a randomized controlled trial by JianLi Wang, Cindy Feng, Mohammad Hajizadeh, Alain Lesage

“…Built upon the research on risk prediction modeling and risk communication, we developed a coach-guided, personalized depression risk communication tool (PDRC) for sharing information about individualized depression risk and evidence-based self-help strategies. …”
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Kun–Qi birth cohort (KQBC) study for the incidence, aetiology and risk factors of chronic kidney disease in Chinese children: a cohort profile by Yan Ma, Xingdong Chen, Jie Wei, Li Guo, Jian Huang, Li Xie, Lei Chen, Ping Lu, Wei Sun, Wei Shi, Feng Ding, Liping Zhang, Yue Zhang, Linlin Zhang, Rui Song, Yi Huang, Jun Shen, Hong Xu, Zhigang Chen, Qian Shen, Ying Ma, Yiwen Zhang, Hongxia Li, Yihui Zhai, Biao Xu, Xian Zhou, Jun Sun, Bing Wu, Rui Su, Xiaoyan Yu, Jianhong Zhu, Yihong Huang, Ling Gong, Nana Wu, Jingjing Sun, Tianyi Liu, He Yuan, Yiwen Shen, Jiaojiao Liu, Xia Han, Juanjuan Wu, Yinghua Sun, Suxiang Xu, Qinyi Zhang, Na Xie, Jianfeng Ji, Weizhi Mo, Yuanxin Liu, Xinghua Shi, Wanshan Qiu, Jiapeng Mi, Juhua Yan, Minghui Zhu, Pei Feng, Wenqian Sheng, Mingqin Tai, Chunqin Zhou, Qian Jin, Chengyu Gu, Xiujuan Huang, Qinfang Qian, Yixin Cao, Haochen Shen, Ziwen Xue, Yucui Ma, Juan Tian, Yingdan Zhang, Weiyuan Zhuang, Kaiqun Yang, Xiaohui Shen, Yanwei Gu, Beibei Shi, Chenhong Pan, Hongmei Zhou, Xiuchen Zhang, Meihua Tang, Kaixuan Gu, Hongping Liu, Yuanxian Li

“…We aim to develop multimodal risk prediction models by integrating genomics and artificial intelligence algorithms. …”
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Machine learning applications in forecasting patient satisfaction and clinical outcomes after carpal tunnel release: a retrospective study by Zohreh Manoochehri, Sara Manoochehri, Seyed Reza Bagheri, Alireza Abdi, Ehsan Alimohammadi

“…This study aimed to develop a machine learning (ML) model to predict post-CTR patient satisfaction and outcomes, serving as a preoperative screening tool. …”
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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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Ambulatory Oxygen for Pulmonary Fibrosis (OxyPuF): a randomised controlled trial and acceptability study by Rachel L Adams, Alisha Maher, Nicola Gale, Anjali Crawshaw, David Thickett, Alice M Turner

“…Traditional qualitative analysis and arts-based coproduction analysis approaches were used to produce a short film. An economic model was planned but could not occur due to early termination. …”
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GLUcose COntrol Safety &amp; Efficacy in type 2 DIabetes, a systematic review and NETwork meta-analysis. by Guillaume Grenet, Shams Ribault, Giao Bao Nguyen, Faustine Glais, Augustin Metge, Thomas Linet, Behrouz Kassai-Koupai, Catherine Cornu, Théodora Bejan-Angoulvant, Sylvie Erpeldinger, Rémy Boussageon, Aurore Gouraud, Fabrice Bonnet, Michel Cucherat, Philippe Moulin, François Gueyffier

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Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications by Can Qi, Can Qi, Ziwei Zhao, Lin Chen, Le Wang, Yun Zhou, Guochen Duan, Guochen Duan

“…A total of five biomarkers,[Selenoprotein P1 (SEPP1), Fibrinogen-like protein 2 (FGL2), NK cell lectin-like receptor K1 (KLRK1), ATP-binding cassette transporters 6(ABCA6) and Galectins(GAL)], were screened, and a risk model based on the biomarkers was created. …”
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Prognostic Risk Signature and Comprehensive Analyses of Endoplasmic Reticulum Stress-Related Genes in Lung Adenocarcinoma by CaiZhen Yang, YuHui Wei, WenTao Li, JinMei Wei, GuoXing Chen, MingPeng Xu, GuangNan Liu

“…A total of 1034 samples from TCGA and GEO were used to screen differentially expressed genes. Further, Random Forest algorithm was utilized to screen characteristic genes related to prognosis. …”
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Joint analysis of single-cell RNA sequencing and bulk transcriptome reveals the heterogeneity of the urea cycle of astrocytes in glioblastoma by Minfeng Tong, Qi Tu, Lude Wang, Huahui Chen, Xing Wan, Zhijian Xu

“…For bulk RNA-seq, univariate Cox and LASSO analyses were undertaken to screen prognostic genes, while multivariate Cox regression analysis was applied to set up a prognostic model. …”
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Effect of miR-200c on inducing autophagy and apoptosis of HT22 cells from mouse hippocampal neurons via regulating PRDM1 protein: a bioinformatics analysis by W. Wu, J. Fu, Q. Liu, Q. Wang, S. Gao, X. Deng, C. Shen

“…The Support Vector Machine (SVM) algorithm in the Weka software was used to process, model, and screen the available miRNA data. …”
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Immune Evasion Mechanism Mediated by ITPRIPL1 and Its Prognostic Implications in Glioma by Zou Xiaoyun, Ye Wenhao, Wu Huan, Yang Yuanyuan, Liu Changqing, Wen Hebao, Ma Caiyun

“…Ninety‐eight machine learning algorithm combinations were screened to identify the optimal predictive model. …”
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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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Case-control study combined with machine learning techniques to identify key genetic variations in GSK3B that affect susceptibility to diabetic kidney diseases by Jinfang Song, Yi Xu, Liu Xu, Tingting Yang, Ya Chen, Changjiang Ying, Qian Lu, Tao Wang, Xiaoxing Yin

“…On the other hand, the expression levels and kinase activity of GSK3β in exosomes differed significantly between patients with different genotypes of the GSK3B, suggesting that the effect of GSK3B gene polymorphisms on GSK3β expression and activity may be an important mechanism leading to individual differences in susceptibility to DKD. XG Boost algorithm model identified rs60393216 and rs1488766 as important biomarkers for clinical early warning of DKD.…”
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Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning by Qiao Tang, Yanwei Ji, Zhongyuan Xia, Yuxi Zhang, Chong Dong, Chong Dong, Qian Sun, Shaoqing Lei

“…The ERDEGs diagnostic model was developed based on a combination of LASSO and Random Forest approaches, and the diagnostic performance was evaluated by the area under the receiver operating characteristic curve (ROC-AUC) and validated against external datasets. …”
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Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope by Ling Guo, PhD, Gregg S. Pressman, MD, Spencer N. Kieu, BS, Scott B. Marrus, MD, PhD, George Mathew, PhD, John Prince, PhD, Emileigh Lastowski, MS, Rosalie V. McDonough, MD, MSc, Caroline Currie, BA, John N. Maidens, PhD, Hussein Al-Sudani, MD, Evan Friend, BA, Deepak Padmanabhan, MD, Preetham Kumar, MD, Edward Kersh, MD, Subramaniam Venkatraman, PhD, Salima Qamruddin, MD

“…Recently, electrocardiogram-based algorithms have shown promise in detecting ALVSD. Objectives: The authors developed and validated a convolutional neural network (CNN) model using single-lead electrocardiogram and phonocardiogram inputs captured by a digital stethoscope to assess its utility in detecting individuals with actionably low ejection fractions (EF) in a large cohort of patients. …”
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Multi-Target Mechanism of Compound Qingdai Capsule for Treatment of Psoriasis: Multi-Omics Analysis and Experimental Verification by Qiao Y, Li C, Chen C, Wu P, Yang Y, Xie M, Liu N, Gu J

“…CQC ingredients-targets network was constructed using these ingredients and their targets. Screening of CQC anti-psoriasis core targets using machine learning algorithm. …”
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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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Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation by Sixuan Wu, Yuanbin Tang, Qihong Pan, Yaqin Zheng, Yeru Tan, Junfan Pan, Yuehua Li

“…In addition, a machine learning model constructed based on Stepglm[backward] with the random forest algorithm achieved the highest C-index (0.999) and screened eight core genes, among which ST14 was noted for its excellent predictive ability. …”
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