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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A systematic review of neural network applications for groundwater level prediction by Samuel K. Afful, Cyril D. Boateng, Emmanuel Ahene, Jeffrey N. A. Aryee, David D. Wemegah, Solomon S. R. Gidigasu, Akyana Britwum, Marian A. Osei, Jesse Gilbert, Haoulata Touré, Vera Mensah

“…This systematic review investigates the application of NNs for GWL prediction, focusing on the architectures of the various NN models employed. The study utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to screen and synthesize relevant scientific articles. …”
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Optimization of Flavor Quality of Lactic Acid Bacteria Fermented Pomegranate Juice Based on Machine Learning by Wenhui ZOU, Fei PAN, Junjie YI, Linyan ZHOU

“…There were 19 key differential volatile compounds screened out by ML. Binary classification models of HWPS and LWPS were established by random forest (RF) and adaptive boosting (AdaBoost) algorithms, and RF algorithm had higher prediction precision and accuracy. …”
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Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images by Juxia Wang, Yu Zhang, Fei Han, Zhenpeng Shi, Fu Zhao, Fengzi Zhang, Weizheng Pan, Zhiyong Zhang, Qingliang Cui

“…The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). …”
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A reproducible approach for the use of aptamer libraries for the identification of Aptamarkers for brain amyloid deposition based on plasma analysis. by Cathal Meehan, Soizic Lecocq, Gregory Penner

“…Eight aptamers were identified as a result of the selection process and screened across 390 plasma samples by qPCR assay. Results were analysed using multiple machine learning algorithms from the Scikit-learn package along with clinical variables including cognitive status, age and sex to create predictive models. …”
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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. …”
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EDITORIAL: ARTIFICIAL INTELLIGENCE AND ITS TRANSFORMATIVE IMPACT ON SCIENTIFIC PUBLISHING by Abubakar Munir

“…Second, the risk of bias in AI algorithms is still an issue because AI algorithms learn from what is already in print form, thus potentially continuing existing biases in publishing materials (5). …”
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Role of Aging in Ulcerative Colitis Pathogenesis: A Focus on ETS1 as a Promising Biomarker by Ni M, Peng W, Wang X, Li J

“…A series of machine learning algorithms was used to screen two feature genes (ETS1 and IL7R) to establish the diagnostic model, which exhibited satisfactory diagnostic efficiency. …”
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Cer(d18:1/16:0) as a biomarkers for acute coronary syndrome in Chinese populations by Liang Zhang, Yang Zhang, YaoDong Ding, Tong Jin, Yi Song, Lin Li, XiaoFang Wang, Yong Zeng

“…The area under the ROC curve was used to screen the most valuable predictor. Distinctive ACS-related variables were screened out using Boruta and LASSO regression. …”
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Systemic immune-inflammatory biomarkers combined with the CRP-albumin-lymphocyte index predict surgical site infection following posterior lumbar spinal fusion: a retrospective stu... by Zixiang Pang, Jiawei Liang, Jiayi Chen, Yangqin Ou, Qinmian Wu, Shengsheng Huang, Shengbin Huang, Yuanming Chen

“…Internal validation employed ROC analysis and calibration curves, while Shapley Additive Explanations (SHAP) values interpreted feature importance in the optimal model.ResultsAmong 2,921 screened patients, 1,272 met inclusion criteria. …”
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人工智能融合临床与多组学数据在卒中防治及医药研发中的应用与挑战Applications and Challenges of Integrating Artificial Intelligence with Clinical and Multi-omics Data in Stroke Prevention, Treatment, and Pharmaceut... by 勾岚，姜明慧，姜勇，廖晓凌，李昊，张杰，程丝 (GOU Lan, JIANG Minghui, JIANG Yong, LIAO Xiaoling, LI Hao, ZHANG Jie, CHENG Si)

“…By integrating and analyzing clinical and multi-omics data, AI technology enhances the identification of high-risk populations, optimizes early diagnosis and risk assessment, enables precise subtyping of stroke, facilitates the screening of potential drug targets, and constructs prognostic prediction models. …”
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Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy by Wuyuan Liao, Wuyuan Liao, Wuyuan Liao, Xinwei Zhou, Xinwei Zhou, Hansen Lin, Hansen Lin, Zihao Feng, Zihao Feng, Xinyan Chen, Yuhang Chen, Yuhang Chen, Minyu Chen, Minyu Chen, Mingjie Lin, Mingjie Lin, Gaosheng Yao, Gaosheng Yao, Jinwei Chen, Jinwei Chen, Haoqian Feng, Haoqian Feng, Yinghan Wang, Yinghan Wang, Zhiping Tan, Zhiping Tan, Youyan Tan, Jun Lu, Jun Lu, Pengju Li, Pengju Li, Jinhuan Wei, Jinhuan Wei, Li Luo, Li Luo, Liangmin Fu, Liangmin Fu, Liangmin Fu

“…Using LASSO regression and multivariable Cox proportional hazards analysis, we constructed a risk model based on these ISRGs. The model's predictive ability was rigorously validated in multiple independent cohorts. …”
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Development and validation of a nomogram for predicting in-hospital mortality in older adult hip fracture patients with atrial fibrillation: a retrospective study by Zhenli Li, Jing He, Tiezhu Yao, Guang Liu, Jing Liu, Ling Guo, Mengjia Li, Mengjia Li, Zhengkun Guan, Zhengkun Guan, Ruolian Gao, Jingtao Ma

“…Logistic regression (LR) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were employed to screen features. We further used Extreme Gradient Boosting (XGBoost) based on features selected by LR and LASSO algorithms to assist in identifying the final model-established features. …”
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Utilizing bioinformatics and machine learning to identify CXCR4 gene-related therapeutic targets in diabetic foot ulcers by Hengyan Zhang, Ye Zhou, Heguo Yan, Changxing Huang, Licong Yang, Yangwen Liu

“…We integrated the genes screened by three machine learning models (LASSO, SVM, and Random Forest), and CXCR4 was identified as a key gene with potential therapeutic value in DFUs. …”
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Exploring ischemic stroke based on the ferroptosis perspective: ECH1 may serve as a new biomarker and therapeutic target by Rendong Qu, Yiyan Zhang, Haojia Zhang, Ke Li, Boning Zhang, Hongxuan Tong, Tao Lu

“…Using differential expression analysis and machine learning algorithms, 12 potential hub genes were successfully screened. …”
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Identification and analysis of neutrophil extracellular trap-related genes in periodontitis via bioinformatics and experimental verification by Miao Yu, Zhenqi Ye, Zixin Ye, Yaping Wu, Xiang Wu

“…Then, machine learning algorithms were exploited to screen hub NRGs, and a predictive model was constructed based on these hub NRGs. …”
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Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome by Ge Jin, Xiaomei Fan, Xiaoliang Liang, Honghong Dai, Jun Wang

“…The risk score model constructed based on these biomarkers could effectively predict the survival rate of cervical cancer patients (AUC >0.6 for 1/3/5 years). …”
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Research on the Evaluation of the Node Cities of China Railway Express Based on Machine Learning by Chenglin Ma, Mengwei Zhou, Wenchao Kang, Haolong Wang, Jiajia Feng

“…The Random Forest model outperformed comparative algorithms with 99.5% prediction accuracy (8.33% higher than conventional classification models), particularly in handling multi-dimensional interactions between urban development factors. …”
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Identifying the NEAT1/miR-26b-5p/S100A2 axis as a regulator in Parkinson's disease based on the ferroptosis-related genes. by Taole Li, Jifeng Guo

“…According to the five machine algorithms, 4 features (S100A2, GNGT1, NEUROD4, FCN2) were screened and used to create a PD diagnostic model. …”
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Maximizing YOLOv2 efficiency: A study on multiclass detection of indoor objects by G Divya Deepak, Subraya Krishna Bhat

“…The objective of the present study is to present a systematic approach for optimizing the key hyperparameters of YOLOv2 model for multiclass object detection, specifically targeting seven classes of indoor objects: chair, fire extinguisher, printer, screen, trash bin, exit, and clock. …”
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