The Application of Machine Learning on Antibody Discovery and Optimization by Jiayao Zheng, Yu Wang, Qianying Liang, Lun Cui, Liqun Wang

“…These machine learning models enable rapid in silico design of antibody candidates within a few days, achieving approximately a 60% reduction in time and a 50% reduction in cost compared to traditional methods. …”
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Application of Support Vector Machines in High Power Device Technology by RAO Wei, LI Yong, YAN Ji

“…As a machine learning algorithm, support vector machine(SVM) has the advantages of good nonlinear processing ability, theoretical global optimum and overcoming the curse of dimensionality. …”
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Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry by Siqi Liu, Ruina Li, Jiayi Zhou, Chaoyuan Dai, Jingui Yu, Qiaoxin Zhang

“…However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that integrates an explicit thermal model with ML algorithms to improve prediction under sparse data conditions. …”
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Primer on machine learning applications in brain immunology by Niklas Binder, Ashkan Khavaran, Roman Sankowski

“…Traditional statistical techniques, adapted for single-cell omics, have been crucial in categorizing cell types and identifying gene signatures, overcoming challenges posed by increasingly complex datasets. We explore how machine learning, particularly deep learning methods like autoencoders and graph neural networks, is addressing these challenges by enhancing dimensionality reduction, data integration, and feature extraction. …”
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Quantifying and Forecasting Emission Reductions in Urban Mobility: An IoT-Driven Bike-Sharing Analysis by Manuel Uche-Soria, Bernardo Tabuenca, Gonzalo Halcón-Gibert, Yilsy Núñez-Guerrero

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Predictive analysis of total organic carbon (TOC) in shale targets: example from the Lower Cretaceous of the Austral Basin (Patagonia, Argentina) using machine learning on outcrop... by Sebastian M. Richiano, Federico Ares

“…Dimensionality reduction improved model performance, with SVC achieving the most robust results. …”
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Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning by Yugal Pande, Jyotismita Chaki

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Developing a Nomogram for Predicting the Probability of Shoulder Redislocation after the First Episode in Young Patients by Chih-Kai Hong, Chin Kuo, Chia-Li Chang, Wei-Ren Su, Ta-Wei Tai

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Investigating reduced-dimensional variability in aircraft-observed aerosol–cloud parameters by Kayley M. Butler, Sam J. Silva, Armin Sorooshian, Richard H. Moore, Glenn S. Diskin, John B. Nowak, Luke Ziemba, Ewan Crosbie, Michael A. Shook, Joshua DiGangi, Edward Winstead, Claire Robinson, Yonghoon Choi

“…Here, we apply two machine learning methods to explore variability in in-situ observations from the NASA ACTIVATE mission. …”
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Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO₂ Emissions Trade-Offs by Yuzhuo Zhang, Jiale Peng, Zi Wang, Meng Xi, Jinlong Liu, Lei Xu

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Predictive modeling of hydrogen production and methane conversion from biomass-derived methane using machine learning and optimisation techniques by Adegboyega Bolu Ehinmowo, Bright Ikechukwu Nwaneri, Joseph Oluwatobi Olaide

“…The study hence established the great opportunity of integration of machine learning models with optimisation techniques in attempts to improve the prediction of hydrogen yield and methane conversion in processes for hydrogen production.…”
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Multi-model radiomics and machine learning for differentiating lipid-poor adrenal adenomas from metastases using automatic segmentation by Shengnan Yin, Ning Ding, Shaocai Wang, Mengjuan Li, Yichi Zhang, Jiacheng Shen, Haitao Hu, Yiding Ji, Long Jin

“…Clinical and imaging features were then incorporated into an XGBoost machine learning model, and model performance was evaluated using Area Under Curve (AUC), accuracy, precision, sensitivity, specificity, and F1 score. …”
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Tropospheric ozone trends and attributions over East and Southeast Asia in 1995–2019: an integrated assessment using statistical methods, machine learning models, and multiple chem... by X. Lu, X. Lu, Y. Liu, Y. Liu, J. Su, J. Su, X. Weng, X. Weng, X. Weng, T. Ansari, Y. Zhang, G. He, G. He, Y. Zhu, Y. Zhu, H. Wang, H. Wang, G. Zeng, G. Zeng, J. Li, J. Li, C. He, C. He, S. Li, S. Li, T. Amnuaylojaroen, T. Butler, Q. Fan, Q. Fan, S. Fan, S. Fan, G. L. Forster, G. L. Forster, M. Gao, J. Hu, Y. Kanaya, M. T. Latif, K. Lu, P. Nédélec, P. Nowack, P. Nowack, B. Sauvage, X. Xu, L. Zhang, K. Li, J.-H. Koo, T. Nagashima

“…<p>We apply a statistical model, two machine learning models, and three chemical transport models to attribute the observed ozone increases over East and Southeast Asia (ESEA) to changes in anthropogenic emissions and climate. …”
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A machine learning model for predicting postoperative complication risk in young and middle-aged patients with femoral neck fractures by Yixin Huang, Yixin Huang, Dongze Lin, Bin Chen, Xiaole Jiang, Shanglin Shangguan, Fengfei Lin

“…Key predictors affecting postoperative complications were identified through LASSO regression and multifactorial logistic regression analyses. Several machine learning (ML) models were then integrated for comparative analysis. …”
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Construction of a prognostic model for endometrial cancer related to programmed cell death using WGCNA and machine learning algorithms by Weicheng Pan, Jinlian Cheng, Shanshan Lin, Qianxi Li, Yuanyuan Liang, Huiying Li, Xianxian Nong, Huizhen Nong

“…To isolate core prognostic PCD-DEGs, methods including consistency clustering analysis, weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and five machine learning techniques for dimensionality reduction were utilized. …”
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Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study by Ran Zhang, Tian Li, Fan Fan, Haoying He, Liuyi Lan, Dong Sun, Zhipeng Xu, Sisi Peng, Jing Cao, Juan Xu, Xiaoxiang Peng, Ming Lei, Hao Song, Junjian Zhang

“…This study aimed to develop and validate an interpretable machine learning (ML) model for VaDep to serve as a clinical support tool. …”
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Limitations of XGBoost in Predicting Material Parameters for Complex Constitutive Models by Prates Pedro, Mitreiro Dário, Andrade-Campos António

“…Machine learning models, particularly Extreme Gradient Boosting, have been explored for predicting material parameters in constitutive models that describe the plastic behaviour of metal sheets. …”
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Predictive Analysis of Cardiovascular Disease Risk Factors in Romania using Machine Learning and Medical Statistics by Radu-Anton MOLDOVAN, Sebastian-Aurelian ŞTEFĂNIGĂ

“…The aim of the present study was to identify and assess the significant risk factors of CVD and develop evidence-based prevention strategies. To do this, we used machine learning algorithms such as logistic regression, random forests, support vector machines (SVM), and artificial neural networks (ANNs) to forecast cardiovascular risk factors from past medical data and epidemiology trends. …”
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Spatiotemporal estimation of near-surface CO2 concentrations over the global continent based on hybrid machine learning model by Xinfeng Huang, Hui Yang, Senwei Qiao, Yuejing Yao, Liu Cui, Huaiwei Fan, Qingzhou Lv, Yina Qiao, Gefei Feng

“…This study proposed a hybrid machine learning model consisting of mix attention (MA) module, deep forest (DF) module and LightGBM to estimate near-surface CO2 concentrations. …”
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NitroNet – a machine learning model for the prediction of tropospheric NO₂ profiles from TROPOMI observations by L. Kuhn, L. Kuhn, S. Beirle, S. Osipov, S. Osipov, A. Pozzer, T. Wagner, T. Wagner

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