Predictive model using systemic inflammation markers to assess neoadjuvant chemotherapy efficacy in breast cancer by Yulu Sun, Yinan Guan, Hao Yu, Yin Zhang, Jinqiu Tao, Weijie Zhang, Yongzhong Yao

“…Survival analysis was performed using the Kaplan-Meier method and log-rank test. A predictive model for pCR was constructed using machine learning algorithms.ResultsAmong the 209 breast cancer patients, 29 achieved pCR. …”
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Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong by William Ka Kei Wu, Tong Liu, Ian Chi Kei Wong, Qingpeng Zhang, Jiandong Zhou, Sharen Lee, Keith Sai Kit Leung, Kamalan Jeevaratnam, Wing Tak Wong

“…The predictive and modeling capabilities of machine/deep learning survival analysis achieved more accurate predictions.…”
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Multilayer analysis of ethnically diverse blood and urine biomarkers for breast cancer risk and prognosis by Jia Feng, Xing Qi, Chen Chen, Baolin Li, Min Wang, Xuelong Xie, Kailan Yang, Xuan Liu, Rui min Chen, Tongtong Guo, Jinbo Liu

“…Notably, we leveraged Luzhou’s clinical data to integrate HDL-C with conventional tumor markers (CEA, CA125, CA153) into a machine learning model, comparing its diagnostic efficacy against tumor marker combination. …”
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Introducing Model for Determining the Center of Mass in Children aged 6 to 12 years old in Isfahan by Akbar Taherian, Masoumeh Shojaei, Afkham Daneshfar, Maryam Sharifdoust

“…For this purpose, their mass was measured with a bascule, variables related to stature with meter and center of mass were measured with a weighing machine based on calculating torques. with respect to the multivariate regression assumptions and stepwise method, the variables that had the greatest impact on the center of mass were selected and models were proposed whit them. …”
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Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food by Juan-Jesús Marín-Méndez, Paula Luri Esplandiú, Miriam Alonso-Santamaría, Berta Remirez-Moreno, Leyre Urtasun Del Castillo, Jaione Echavarri Dublán, Eva Almiron-Roig, María-José Sáiz-Abajo

“…Ten different models were screened (Multivariate Linear regression, Lasso regression, Rigde regression, Elastic Net regression, K-Neighbors regression, Decision trees regression, Partial Least Square, Support Vector Machines, Gradient Boosting regression and Random Forest regression). …”
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Novel exosome-associated LncRNA model predicts colorectal cancer prognosis and drug response by Chi Zhou, Qian Qiu, Xinyu Liu, Tiantian Zhang, Leilei Liang, Yihang Yuan, Yufo Chen, Weijie Sun

“…Next, we further provide colony formation assay, Transwell assay and xenograft models to understand the carcinogenic effect of MIR4713HG. …”
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Clinical-genomic characteristics of homologous recombination deficiency (HRD) in breast cancer: application model for practice by Jinsui Du, Lizhe Zhu, Chenglong Duan, Nan Ma, Yudong Zhou, Danni Li, Jianing Zhang, Jiaqi Zhang, Yalong Wang, Xi Liu, Yu Ren, Bin Wang

“…Methods A total of 93 breast cancer patients who underwent HRD genetic testing were included in the study. According to the machine learning model called genomic scar (GS) HRD was defined as a genomic scar score (GSS) ≥ 50 or with deleterious mutation in the BRCA. …”
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Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort by Tahzeeb Fatima, Yuan Zhang, Georgios K. Vasileiadis, Araz Rawshani, Ronald van Vollenhoven, Jon Lampa, Bjorn Gudbjornsson, Espen A. Haavardsholm, Dan Nordström, Gerdur Gröndal, Kim Hørslev-Petersen, Kristina Lend, Marte S. Heiberg, Merete Lund Hetland, Michael Nurmohamed, Mikkel Østergaard, Till Uhlig, Tuulikki Sokka-Isler, Anna Rudin, Cristina Maglio

“…Fifteen features were identified using machine learning-based multivariable selection. The best predictive model using logistic regression achieved AUC of 0.75 in training and 0.73 in the test set. …”
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Joint Modeling of Quasar Variability and Accretion Disk Reprocessing Using Latent Stochastic Differential Equations by Joshua Fagin, James Hung-Hsu Chan, Henry Best, Matthew O’Dowd, K. E. Saavik Ford, Matthew J. Graham, Ji Won Park, V. Ashley Villar

“…We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. …”
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Identification of the Optimal Model for the Prediction of Diabetic Retinopathy in Chinese Rural Population: Handan Eye Study by Shanshan Jin, Xu Zhang, Hanruo Liu, Jie Hao, Kai Cao, Caixia Lin, Mayinuer Yusufu, Na Hu, Ailian Hu, Ningli Wang

“…Five algorithms, including multivariable logistic regression (MLR), classification and regression trees (C&RT), support vector machine (SVM), random forests (RF), and gradient boosting machine (GBM), were used to establish DR prediction models with HES data. …”
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Evaluation of early donor renal function recovery after living-donor nephrectomy as a predictor of allograft failure after kidney transplantation: a longitudinal cohort and machine... by Saifu Yin, Xing Li, Chengze Liang, Rong Bao, Jun Zeng, Ming Ma, Mengli Zhu, Xianding Wang, Lijuan Wu, Turun Song, Tao Lin

“…Model performance was assessed using Area Under the Receiver Operating Characteristic (ROC) curve (AUC). …”
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Exposure to hair metals and metal-mixtures associated with blood lipids and dyslipidemia in Chinese adults: Evidence from a national cross-sectional study by Yunjiang Yu, Wenjie Meng, Xiaohui Zhu, Zongrui Li, Tong Zheng, Ping He, Ying Yu, Chenyin Dong, Zhenchi Li, Hongxuan Kuang, Mingdeng Xiang, Xiaodi Qin, Yang Zhou

“…Bayesian Kernel Machine Regression (BKMR) models were applied to assess the combined and interactive effects of metal mixtures on dyslipidemia. …”
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Association between urinary cadmium levels and increased gallstone disease in US adults by Zhaowei Wu, Shiming Jiang, Jinzhi Li, Panguo Wang, Yong Chen

“…Additionally, urinary cadmium levels were associated with an increased risk of gallstone formation in young individuals, males, Mexican Americans, Non-Hispanic Whites, as well as smokers and drinkers. Moreover, nine machine learning methods were utilized to construct an interpretable predictive model for gallstone prevalence. …”
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Effects of Stretch-Bending Straightening on the Tensile Properties of Cold Rolled Packaging Steel by Sebastian Möller, Dimitrios Nouskalis, Björn Ehmke, Dirk Matusch, Christian Dresbach, Holm Altenbach

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Comparison of Off-the-Shelf Methods and a Hotelling Multidimensional Approximation for Data Drift Detection by J. Ramón Navarro-Cerdán, Vicent Ortiz Castelló, David Millán Escrivá

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Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram construction by Kui Tu, Dan Luo, Xuanyu Gu, Jichang Jiang, Zhihong Zheng, Lijin Zhao

“…Abstract Background To identify risk factors for post-transplant mortality and develop a machine learning-integrated prognostic tool to optimise clinical decision-making in liver transplantation (LT) recipients. …”
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Internal Climate Variability Obscures Future Freezing Rain Changes Despite Global Warming Trend by Haoyu (Richard) Zhuang, Arthur T. DeGaetano, Flavio Lehner

“…Here, we introduce a framework utilizing a novel machine‐learning algorithm to diagnose freezing rain in reanalysis and climate model simulations. …”
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Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas by Bowen Wang, Bowen Wang, Mengjia Peng, Yan Li, Jinhang Gao, Tao Chang

“…Multiple machine learning methods were applied to validate the model. …”
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Application of Response Surface Methodology and Central Composite Inscribed Design for Modeling and Optimization of Marble Surface Quality by Sümeyra Cevheroğlu Çıra, Ahmet Dağ, Askeri Karakuş

“…This study has shown that the CCI could efficiently be applied for the modelling of polishing machine for surface quality of marble strips. …”
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Development and validation of a nomogram for predicting lung cancer based on acoustic–clinical features by Zhou Lu, Zhou Lu, Jiaojiao Sha, Xunxia Zhu, Xiaoyong Shen, Xiaoyu Chen, Xin Tan, Rouyan Pan, Shuyi Zhang, Shi Liu, Tao Jiang, Jiatuo Xu

“…Furthermore, the nomogram model was compared with predictive models that were developed using six additional machine-learning (ML) methods.ResultsOur acoustic–clinical nomogram model demonstrated a strong discriminative ability, with AUCs of 0.774 (95% confidence interval [CI], 0.716–0.832) and 0.714 (95% CI: 0.616–0.811) in the training and test sets, respectively. …”
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