A hybrid prediction and multi-objective optimization framework for limestone calcined clay cement concrete mixture design by Xi Chen, Weiyi Chen, Zongao Li, Pu Zhang

“…A dataset of 387 LC3 specimens was constructed to develop ML models for predicting compressive strength. Multivariate Imputation by Chained Equations-Extreme Gradient Boosting (MICE-XGBoost) model achieved the highest accuracy of R2 = 0.928 (± 0.009). …”
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The prognostic value of immunoscore in the early-onset colorectal cancer by Xinchun Wu, Sen Hou, Yingjiang Ye, Zhidong Gao

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Bioinformatics Analysis Identifies Lipid Droplet‐Associated Gene Signatures as Promising Prognostic and Diagnostic Models for Endometrial Cancer by Vijayalakshmi N. Ayyagari, Miao Li, Paula Diaz‐Sylvester, Kathleen Groesch, Teresa Wilson, Ejaz M. Shah, Laurent Brard

“…Model performance was evaluated using ROC curves and support vector machine (SVM) classification. …”
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A novel UHPLC-HRMS method for simultaneous determination of 20 amino metabolites and proteins in lymphoma patients’ cells and serum by Mei Zhang, Qingkun Ma, Han Zhao, Ge Sun, Tenghua Zhao, Yuqing Wang, Shiji Chen, Lele Jia, Yixiang Song, Yanling Mu

“…Further, up-regulated haptoglobin, coagulation factor VII and catalase could directly negatively regulate Ala, Lys and Phe, which caused Trp, His, Ser, Asp and Pro expression decreased significantly in lymphoma patients (p < 0.05). Ultimately, a machine learning model was established to predict lymphoma with accuracy rate of 93.68%. …”
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Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming by Lei Gu, Jiabo Yin, Louise J. Slater, Jie Chen, Hong Xuan Do, Hui‐Min Wang, Lu Chen, Zhiqiang Jiang, Tongtiegang Zhao

“…Here, we integrate bias‐corrected climate experiments, multiple hydrological models (HYs), and a multivariate analysis of variance (ANOVA) with a machine learning modeling framework, to examine the evolving frequency and multivariate characteristics of hydrological droughts and their mechanisms under climate warming for 6,688 catchments in the five principal Köppen‐Geiger climate zones. …”
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Comparative Analysis of Time-Series Forecasting Models for eLoran Systems: Exploring the Effectiveness of Dynamic Weighting by Jianchen Di, Miao Wu, Jun Fu, Wenkui Li, Xianzhou Jin, Jinyu Liu

“…This paper presents an advanced time-series forecasting methodology that integrates multiple machine learning models to improve data prediction in enhanced long-range navigation (eLoran) systems. …”
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Host and bacterial urine proteomics might predict treatment outcomes for immunotherapy in advanced non-small cell lung cancer patients by David Dora, Peter Revisnyei, Peter Revisnyei, Alija Pasic, Gabriella Galffy, Edit Dulka, Anna Mihucz, Brigitta Roskó, Sara Szincsak, Anton Iliuk, Glen J. Weiss, Zoltan Lohinai, Zoltan Lohinai

“…Internal validation was performed using the Random Forest (RF) machine learning (ML) algorithm. RF was validated with a non-linear Bayesian ML model. …”
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SiMBA-Augmented Physics-Informed Neural Networks for Industrial Remaining Useful Life Prediction by Min Li, Jianfeng Qin, Haifeng Fan, Ting Ke

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Prediction of Vigor of Naturally Aged Seeds from Xishuangbanna Cucumber (<i>Cucumis sativus</i> L. var. <i>xishuangbannanesis</i>) Using Hyperspectral Imaging by Meng Zhang, Jiangping Song, Huixia Jia, Xiaohui Zhang, Wenlong Yang, Yang Wang, Haiping Wang

“…Combined with KNN (K-Nearest Neighbor) and LogitBoost algorithms, predictive models for seed viability were established. The results demonstrated that the L2NN-KNN model outperformed other models, achieving an accuracy of 83.33%, precision of 86.99%, and an F1-score of 0.83. …”
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Risk Factors and Predictive Model for Ischemic Complications in Endovascular Treatment of Intracranial Aneurysms: Insights From a Large Patient Cohort by Jianwen Jia, Zeping Jin, Mirzat Turhon, Yixin Lin, Xinjian Yang, Yang Wang, Yunpeng Liu

“…The predictive model, derived from the multivariate regression analysis results, demonstrated robust reliability. …”
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Novel composite health assessment risk model for older allogeneic transplant recipients: BMT-CTN 1704 by Mohamed L. Sorror, Wael Saber, Brent Logan, Nancy Geller, Anna Bellach, Jianqun Kou, William Wood, John M. McCarty, Thomas G. Knight, Lyndsey Runaas, Laura Johnston, Jeremy Walston, Ryotaro Nakamura, Lori Jarrett, Asmita Mishra, Joseph Uberti, Parastoo B. Dahi, Jennifer N. Saultz, Shannon R. McCurdy, Lawrence E. Morris, Philip H. Imus, William J. Hogan, Kalyan Nadiminti, Vijaya Raj Bhatt, Rebecca Olin, Joseph Maakaron, Ronald Sobecks, Sarah A. Wall, Deborah Mattila, Bailey Protz, Steven M. Devine, Mary M. Horowitz, Andrew S. Artz

“…Two machine learning (ML) models (Cox and pseudo-value boosting) were also explored. …”
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Retrospective cohort study based on the MIMIC-IV database: analysis of factors influencing all-cause mortality at 30 days, 90 days, 1 year, and 3 years in patients with different t... by Xuehui Fan, Jing Xu, Ruixue Ye, Qiu Zhang, Yulong Wang

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Evaluation and Anomaly Detection Methods for Broadcast Ephemeris Time Series in the BeiDou Navigation Satellite System by Jiawei Cai, Jianwen Li, Shengda Xie, Hao Jin

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Research on the application of distinguishing between benign and malignant breast nodules using MRI and US radiomics by Yifan Liu, Dan Zhou, Jing Liu, Jinding Wei, Xiao Hu, Xiaoli Yu

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Early prediction of progression-free survival of patients with locally advanced nasopharyngeal carcinoma using multi-parametric MRI radiomics by Lian Jian, Cai Sheng, Huaping Liu, Handong Li, Pingsheng Hu, Zhaodong Ai, Xiaoping Yu, Huai Liu

“…Clinical and combined models were developed via multivariate Cox regression model. …”
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Comparative study of XGBoost and logistic regression for predicting sarcopenia in postsurgical gastric cancer patients by Yajing Gu, Shu Su, Xianping Wang, Juanjuan Mao, Xuan Ni, Ai Li, Yueli Liang, Xing Zeng

“…In summary, the machine learning-based sarcopenia prediction model constructed in this study provides a valuable decision support tool for clinical screening and intervention of sarcopenia.…”
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Association between multiple serum biomarkers and cerebral vasospasm in patients with aneurysmal subarachnoid hemorrhage by Chenghao Yang, Fei Luo, Xi Chen, Yunliang Deng, Meirui Lan

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Establishment of an alternative splicing prognostic risk model and identification of FN1 as a potential biomarker in glioblastoma multiforme by Xi Liu, Jinming Song, Zhiming Zhou, Yuting He, Shaochun Wu, Jin Yang, Zhonglu Ren

“…The eleven genes (C2, COL3A1, CTSL, EIF3L, FKBP9, FN1, HPCAL1, HSPB1, IGFBP4, MANBA, PRKAR1B) were screened to develop an alternative splicing prognostic risk score (ASRS) model through machine learning algorithms. The model was trained on the TCGA-GBM cohort and validated with four external datasets from CGGA and GEO, achieving AUC values of 0.808, 0.814, 0.763, 0.859, and 0.836 for 3-year survival rates, respectively. …”
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Optimized Coupling Coil Geometry for High Wireless Power Transfer Efficiency in Mobile Devices by Fahad M. Alotaibi

“…The framework integrates a hybrid sequential neural network and multivariate regression model to optimize coil winding and ferrite core geometry. …”
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Unveiling geological complexity in the Serra Dourada Granite using self-organizing maps and hierarchical clustering: Insights for REE prospecting in the Goiás Tin Province, Brasíli... by Guilherme Ferreira da Silva, Marcos Vinícius Ferreira, Lucy Takehara Chemale, Igor Vasconcelos Santana, Nilson Francisquini Botelho

“…Our findings underscore the value of machine learning techniques, particularly SOM, in geoscientific data analysis. …”
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