Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospec... by Kohji Nishida, Ryo Kawasaki, Yiming Qian, Liangzhi Li, Yuta Nakashima, Hajime Nagahara

“…This two-stage approach provides human interpretable information between stages, which helps clinicians gain insights into the screening process copiloting with the DL model.…”
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Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer by Quan Yuan, Rongjie Ye, Yao Qian, Hao Yu, Yuexin Zhou, Xiaoqiao Cui, Feng Liu, Ming Niu

“…The Least Absolute Shrinkage and Selection Operator (LASSO) Cox algorithm, combined with XGBoost and Random Forest (RF) models, identified 9 overlapping prognostic features, enhancing the nomogram’s predictive accuracy for overall survival (OS). …”
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An Upper Partial Moment Framework for Pathfinding Problem Under Travel Time Uncertainty by Xu Zhang, Mei Chen

“…Theoretical analysis shows that the MUPM framework is consistent with the expected utility theory (EUT) and stochastic dominance theory (SDT), providing a behavioral foundation for the model. To efficiently solve the model, an SDT-based label-correcting algorithm is adapted, with a pre-screening step to reduce unnecessary pairwise path comparisons. …”
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Global trends in machine learning applications for single-cell transcriptomics research by Xinyu Liu, Zhen Zhang, Chao Tan, Yinquan Ai, Hao Liu, Yuan Li, Jin Yang, Yongyan Song

“…Research hotspots concentrated on random forest (RF) and deep learning models, showing transition from algorithm development to clinical applications (e.g., tumor immune microenvironment analysis). …”
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Predicting diabetic peripheral neuropathy through advanced plantar pressure analysis: a machine learning approach by Mehewish Musheer Sheikh, Mamatha Balachandra, Narendra V. G., Arun G. Maiya

“…An automated image processing algorithm segmented plantar pressure images into forefoot and hindfoot regions for precise pressure distribution measurement. …”
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An Automatic Measurement Method of Test Beam Response Based on Spliced Images by Dong Liang, Jing Liu, Lida Wang, Chenjing Liu, Jia Liu

“…Next, the spliced image is obtained through the PCA-SIFT method with a screening mechanism. The cracks’ information is acquired by the dual network model. …”
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Visual impairments associated with the treatment of malignant tumors of the female reproductive system: a literature review and practical recommendations for oncogynecologists by Y. A. Chuikova, O. A. Odintsova, D. V. Skalozub, A. G. Antonyuk, A. A. Kabartai, A. N. Nurmamatova, R. M. Azimov, P. A. Snatenkova, A. A. Silantyev, S. R. Osina, D. S. Minasyan, S. A. Lashevich, I. A. Isaeva, E. A. Rukhlyadyeva

“…Practical recommendations are provided for screening, monitoring, and managing patients at risk of ocular complications, including referral algorithms and treatment modification strategies. …”
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Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach by Yongjiu Feng, Haoteng Li, Xiaohua Tong, Pengshuo Li, Rong Wang, Shurui Chen, Mengrong Xi, Jingbo Sun, Yuhao Wang, Huaiyu He, Chao Wang, Xiong Xu, Huan Xie, Yanmin Jin, Sicong Liu

“…The combined use of CNN and SHAP enables more effective potential site screening and a deeper understanding of the factors influencing selection. …”
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High-flow nasal cannula therapy versus continuous positive airway pressure for non-invasive respiratory support in paediatric critical care: the FIRST-ABC RCTs by Padmanabhan Ramnarayan, Alvin Richards-Belle, Karen Thomas, Laura Drikite, Zia Sadique, Silvia Moler Zapata, Robert Darnell, Carly Au, Peter J Davis, Izabella Orzechowska, Julie Lester, Kevin Morris, Millie Parke, Mark Peters, Sam Peters, Michelle Saull, Lyvonne Tume, Richard G Feltbower, Richard Grieve, Paul R Mouncey, David Harrison, Kathryn Rowan

“…Background Despite the increasing use of non-invasive respiratory support in paediatric intensive care units, there are no large randomised controlled trials comparing two commonly used non-invasive respiratory support modes, continuous positive airway pressure and high-flow nasal cannula therapy. …”
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Immunoglobulin G N-Glycosylation and Inflammatory Factors: Analysis of Biomarkers for the Diagnosis of Moyamoya Disease by Zan X, Liu C, Wang X, Sun S, Li Z, Zhang W, Sun T, Hao J, Zhang L

“…This research aimed to evaluate the diagnostic efficacy of IgG N-glycosylation for MMD.Methods: Ultra-high-performance liquid chromatography (UPLC) was employed to examine the properties of IgG N-glycans in blood samples from 116 patients with MMD and 126 controls, resulting in the quantitative determination of 24 initial glycan peaks (GP). Through the Lasso algorithm and multivariate logistic regression analysis, we constructed a diagnostic model based on initial glycans and related inflammatory factors to distinguish MMD patients from healthy individuals.Results: After adjusting for potential confounding variables, including age, fasting blood glucose (FBG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), neutrophil count (NEUT), and lymphocyte count (LYM), our study demonstrated significant differences in the characteristics of 6 initial glycans and 16 derived glycans between the MMD cohort and the healthy control group. …”
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Leveraging diverse cell-death patterns to predict to predict prognosis and immunotherapy in hepatocellular carcinoma by Xiaoxiang Zhang, Dongxiao Ding, Dianqian Wang, Yunsheng Qin

“…Although many efforts have been made to improve the prognosis of LIHC, the situation is still dismal. …”
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Diagnostic Value of Glycosylated Extracellular Vesicle microRNAs in Gastric Cancer by Wang S, Ma C, Ren Z, Zhang Y, Hao K, Liu C, Xu L, He S, Zhang J

“…The signatures were screened in a discovery cohort of GC patients (n=55) and non-disease controls (n=46) using an integrated process, including high-throughput sequencing technology, screening using a complete bioinformatics algorithm, validation using RT-qPCR, and evaluation by constructing a diagnostic model. …”
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Research on Path Optimization of Vehicle-Drone Joint Distribution considering Customer Priority by Xiaoye Zhou, Yuhao Feng

“…Compared with the three algorithms and error analysis, the effectiveness of the model and the two-stage algorithm was verified. …”
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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

“…By incorporating the interaction between TMB and the TME, our model provides a more accurate prediction of patient prognosis and treatment response across multiple cancer types. …”
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Optimization of the Canopy Three-Dimensional Reconstruction Method for Intercropped Soybeans and Early Yield Prediction by Xiuni Li, Menggen Chen, Shuyuan He, Xiangyao Xu, Panxia Shao, Yahan Su, Lingxiao He, Jia Qiao, Mei Xu, Yao Zhao, Wenyu Yang, Wouter H. Maes, Weiguo Liu

“…Point cloud preprocessing was refined through the application of secondary transformation matrices, color thresholding, statistical filtering, and scaling. Key algorithms—including the convex hull algorithm, voxel method, and 3D α-shape algorithm—were optimized using MATLAB, enabling the extraction of multi-dimensional canopy parameters. …”
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Dynamic SOFA component scores-based deep learning for short to long-term mortality prediction in sepsis survivors by Juan Wei, Feihong Lin, Tian Jin, Qian Yao, Sheng Wang, Di Feng, Xin Lv, Wen He

“…Comparisons were made with a multilayer perceptron and two machine learning models of random forest and eXtreme Gradient Boosting (XGBoost). …”
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Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives—A Comprehensive Review by Stefano Di Michele, Anna Maria Fulghesu, Elena Pittui, Martina Cordella, Gilda Sicilia, Giuseppina Mandurino, Maurizio Nicola D’Alterio, Salvatore Giovanni Vitale, Stefano Angioni

“…Studies on diagnostic criteria, imaging modalities, stromal assessment, and machine-learning algorithms were prioritized. Additional references were identified via citation screening. …”
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Identification of core therapeutic targets for Monkeypox virus and repurposing potential of drugs: A WEB prediction approach. by Huaichuan Duan, Quanshan Shi, Xinru Yue, Zelan Zhang, Ling Liu, Yueteng Wang, Yujie Cao, Zuoxin Ou, Li Liang, Jianping Hu, Hubing Shi

“…Here, we first summarized and improved the open reading frame information of monkeypox, constructed the monkeypox inhibitor library and potential targets library by database research as well as literature search, combined with advanced protein modeling technologies (Sequence-based and AI algorithms-based homology modeling). …”
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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. …”
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Potential Metabolic Markers in the Tongue Coating of Chronic Gastritis Patients for Distinguishing Between Cold Dampness Pattern and Damp Heat Pattern in Traditional Chinese Medici... by Yuan S, Zhang R, Zhu Z, Zhou X, Zhang H, Li X, Hao Y

“…We applied metabolomics to identify differential metabolites distinguishing these patterns.Methods: In this study, the first principal component was analyzed by the OPLS-DA model. The model quality was evaluated by 7-fold cross-validation, and the model validity was evaluated based on R²Y (interpretability of categorical variable Y) and Q² (predictability of the model), and the permutation test was used for further verification. …”
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