Type 1 diabetes prevention clinical trial simulator: Case reports of model‐informed drug development tool by Juan Francisco Morales, Marian Klose, Yannick Hoffert, Jagdeep T. Podichetty, Jackson Burton, Stephan Schmidt, Klaus Romero, Inish O'Doherty, Frank Martin, Martha Campbell‐Thompson, Michael J. Haller, Mark A. Atkinson, Sarah Kim

“…To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. …”
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Development and validation of a pathological model predicting the efficacy of neoadjuvant therapy for breast cancer based on RCB scoring by Huan Li, Xianli Ju, Chuanfei Zeng, Zhengzhuo Chen, LinXin Yu, Ge Ke, Ziyin Huang, Youping Wang, Jingping Yuan, Mingkai Chen

“…Based on clinical and pathological characteristics along with the Residual Cancer Burden (RCB) score, we utilized a support vector machine (SVM) algorithm to construct a Pathomics Breast Cancer Signature (PBCS) prediction model. …”
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Distribution mapping and risk assessment of lead in topsoil across the Tibetan Plateau by Linglong Chen, Ruxia Li, Ru Zhang, Yi Yang, Yonghua Li

“…Multivariate analysis identified soil pH and terrain slope as the primary environmental determinants on lead accumulation. …”
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Segmental multi-frequency bioelectrical impedance analysis indicates dehydration status after exercise by Shuang Wang, Pengfei Zhang, Yang Hu, Yao Zheng, Hongyan Yang, Jiaheng Zhou, Xuyun Liu, Jie Xu, Hui Li, Yang Liu, Jia Li, Xing Zhang, Jing Lou, Ling Dong, Guiling Wu

“…Correlation analysis revealed that the changes of BI (ΔZ) in arms and legs during exercise were correlated with the BWL. Multivariate regression and machine learning analysis showed that predicted values of dehydration were correlated with true values of dehydration (ΔBW: R2 = 0.4471, R2 = 0.5502; ΔBW%: R2 = 0.4469, R2 = 0.5073). …”
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Elaboration of principles for developing technological systems for the finishing of gears by Yuriy Danylchenko, Michael Storchak

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Utility of Domain Adaptation for Biomass Yield Forecasting by Jonathan M. Vance, Bryan Smith, Abhishek Cherukuru, Khaled Rasheed, Ali Missaoui, John A. Miller, Frederick Maier, Hamid Arabnia

“…This forecasting technique generally provides more accurate forecasts than the established ARIMA family of forecasters for both univariate and multivariate time series. Furthermore, this ML-based technique is potentially easier to use than the ARIMA family of models. …”
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Potassium Fulvate Alleviates Salinity and Boosts Oat Productivity by Modifying Soil Properties and Rhizosphere Microbial Communities in the Saline–Alkali Soils of the Qaidam Basin... by Jie Wang, Xin Jin, Xinyue Liu, Yunjie Fu, Kui Bao, Zhixiu Quan, Chengti Xu, Wei Wang, Guangxin Lu, Haijuan Zhang

“…Structural equation modeling further showed that PF mitigates salinity chiefly by improving soil physicochemical properties (path coefficient = −0.77; <i>p</i> < 0.001), with microbial assemblages acting as key intermediaries. …”
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Covariate-adjusted construction of gene regulatory networks using a combination of generalized linear model and penalized maximum likelihood. by Omid Chatrabgoun, Alireza Daneshkhah, Parisa Torkaman, Mark Johnston, Nader Sohrabi Safa, Ali Kashif Bashir

“…To reach out this goal, we apply a generalized linear model (GLM) in first step and later a penalized maximum likelihood to construct the gene regulatory network using Glasso technique for the residuals of a multi-level multivariate GLM among the gene expressions of one species as a multi-levels response variable and the gene expression of related species as a multivariate covariates. …”
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Elucidating epigenetic landscape of gastric premalignant lesions through genome‐wide mapping of 5‐hydroxymethylcytosines: A 12‐year median follow‐up study by Zhongguang Luo, Wenshuai Li, Wanwei Zheng, Yixiang Shi, Maolin Ye, Xiangyu Guo, Kaiyi Fu, Changsheng Yan, Bowen Wang, Bin Lv, Shaocong Mo, Hongyang Zhang, Jun Zhang, Chuan He, Feifei Luo, Wei Zhang, Jie Liu

“…An exploratory study was conducted to summarize a 5hmC‐based epigenetic model for predicting cancer progression using multivariable logistic regression and machine learning. …”
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Enhancing drought monitoring through regional adaptation: Performance and calibration of drought indices across varied climatic zones of Iran by Saeed Sharafi, Fatemeh Omidvari, Fatemeh Mottaghi

“…Further research is essential to refine these models and integrate advanced methodologies, such as machine learning (ML), to enhance drought prediction accuracy and support climate adaptation efforts.…”
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Predicting pain and its association with mortality in patients with stroke by Adam Viktorisson, Aref Haj Hashem, Katharina S Sunnerhagen, Tamar Abzhandadze

“…Twelve (out of 28) predictor variables were selected by three machine learning methods, and a multivariable binary logistic regression model was fitted for predicting PSP. …”
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Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery by Wenlong Fu, Jiawen Tan, Xiaoyuan Zhang, Tie Chen, Kai Wang

“…Afterwards, a hybrid optimization algorithm combining mutation operator, grey wolf optimizer (GWO), and sine cosine algorithm (SCA), termed mutation hybrid GWO-SCA (MHGWOSCA), was proposed for parameter selection of support vector machine (SVM). The optimal SVM model was later employed to classify different fault samples. …”
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A Short-Term Power Output Forecasting Model Based on Correlation Analysis and ELM-LSTM for Distributed PV System by Deng Yongsheng, Jiao Fengshun, Zhang Jie, Li Zhikeng

“…Then, based on the multimodel univariate extreme learning machine (ELM) submodel and the single-model multivariate long short-term memory (LSTM) submodel, the ELM-LSTM model is established. …”
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Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm by Qiyuan Cui, Jianhong Pu, Wei Li, Yun Zheng, Jiaxi Lin, Lu Liu, Peng Xue, Jinzhou Zhu, Mingqing He

“…ObjectiveThe aim of this study was to develop and validate a machine learning-based model to predict the development of impaired fasting glucose (IFG) in middle-aged and older elderly people over a 5-year period using data from a cohort study.MethodsThis study was a retrospective cohort study. …”
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High density of TCF1+ stem-like tumor-infiltrating lymphocytes is associated with favorable disease-specific survival in NSCLC by Dagny Førde, Thomas Kilvær, Thomas Kilvær, Mona Irene Pedersen, Egil S Blix, Ilona Urbarova, Erna-Elise Paulsen, Erna-Elise Paulsen, Mehrdad Rakaee, Mehrdad Rakaee, Mehrdad Rakaee, Lill-Tove Rasmussen Busund, Lill-Tove Rasmussen Busund, Tom Donnem, Tom Donnem, Sigve Andersen, Sigve Andersen

“…However, less is known about the survival benefits oftheir subpopulations.MethodsUsing machine learning models, we assessed the clinical association of the CD8+, PD1+, TCF1+ cel l subset by multiplex immunohistochemistry using tissue microarrays in 553 non-small cell lung cancer (NSCLC) patients and its correlation with other immune cell biomarkers.ResultsWe observed positive correlations between TCF1 and CD20 (r=0.37), CD3 (r=0.45)and CD4 (r=0.33). …”
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γ‐Glutamyl Transferase and Long‐Term Survival in the SYNTAXES Trial: Is It Just the Liver? by Kai Ninomiya, Patrick W. Serruys, Scot Garg, Shigetaka Kageyama, Nozomi Kotoku, Shinichiro Masuda, Pruthvi C. Revaiah, Neil O'leary, Arie Pieter Kappetein, Michael J. Mack, David R. Holmes, Piroze M. Davierwala, Friedrich W. Mohr, Daniel J. F. M. Thuijs, Yoshinobu Onuma

“…The mean values of GGT for men and women were 43.5 (SD, 48.5) and 36.4 (SD, 46.1) U/L, respectively. In multivariable Cox regression models adjusted by traditional risk factors, GGT was an independent predictor for all‐cause death at 10‐year follow‐up, and each SD increase in log‐GGT was associated with a 1.24‐fold risk of all cause death at 10‐year follow‐up (95% CI, 1.10–1.40). …”
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Cardiovascular Risk Factors as Independent Predictors of Diabetic Retinopathy in Type II Diabetes Mellitus: The Development of a Predictive Model by Cristian Dan Roşu, Melania Lavinia Bratu, Emil Robert Stoicescu, Roxana Iacob, Ovidiu Alin Hațegan, Laura Andreea Ghenciu, Sorin Lucian Bolintineanu

“…Several machine learning models, including Random Forest, XGBoost, and Support Vector Machines, were applied to assess the predictive value of cardiovascular risk factors and identify key predictors. …”
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A Prediction Model for Sight-Threatening Diabetic Retinopathy Based on Plasma Adipokines among Patients with Mild Diabetic Retinopathy by Yaxin An, Bin Cao, Kun Li, Yongsong Xu, Wenying Zhao, Dong Zhao, Jing Ke

“…The accuracy of the multivariate SVM classification model was acceptable in both the training set (AUC=0.81, sensitivity=71%, and specificity=91%) and the testing set (AUC=0.77, sensitivity=61%, and specificity=92%). 110 T2DM patients with mild NPDR, the high-risk population of STDR, were enrolled for external validation. …”
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Immunogenic cell death genes in single-cell and transcriptome analyses perspectives from a prognostic model of cervical cancer by Li Ning, Li Ning, Xiu Li, Xiu Li, Yating Xu, Yating Xu, Yu Si, Yu Si, Hongting Zhao, Qinling Ren, Qinling Ren

“…Multivariate analysis demonstrated that low-risk patients had significantly better overall survival compared to high-risk patients, confirming the model as an independent prognostic tool. …”
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Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection by Mohammad Abrar, Abdu Salam, Ahmed Albugmi, Fahad Al-otaibi, Farhan Amin, Isabel de la Torre, Thania Candelaria Chio Montero, Perla Araceli Arroyo Gala

“…In future work, we suggest incorporating large-size datasets that include more diverse patient groups and refining the model with advanced machine-learning models and techniques.…”
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