Development of a Diagnostic Model for Focal Segmental Glomerulosclerosis: Integrating Machine Learning on Activated Pathways and Clinical Validation by Ge Y, Liu X, Shu J, Jiang X, Wu Y

“…We then developed a highly accurate diagnostic model by integrating nine machine learning algorithms into 101 combinations, achieving near-perfect AUC values across training, validation, and external cohorts. …”
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High incidence of glucocorticoid-induced hyperglycaemia in inflammatory bowel disease: metabolic and clinical predictors identified by machine learning by Markus Gwiggner, Louise Downey, Florina Borca, Martin McDonnell, Richard Felwick, Suranga Dharmasiri, Tilly Mills, Trevor R Smith, J R Fraser Cummings, Mayank Patel, Richard J Harris, Benjamin Zare, Matt Stammers, Hang T T Phan

“…Therefore, CBG monitoring should be included in routine clinical practice. Machine learning methods can identify key risk factors for clinical complications. …”
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Real-Time Defect Detection for Fast-Moving Fabrics on Circular Knitting Machine Under Various Illumination Conditions by Yan-Qin Ni, Pei-Kai Huang, Ching-Han Yang, Chin-Chun Chang, Wei-Jen Wang, Deron Liang

“…In industrial production, automated inspection methods for circular knitting machines often encounter several challenges. First, the rapid movement of fabrics on these machines makes it difficult for existing fabric defect detection methods to effectively capture and process the motion. …”
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Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning by Yi Dong, Xinting Wang, Sheng Wang, Baoguo Li, Junming Liu, Jianxi Huang, Xuecao Li, Yelu Zeng, Wei Su

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Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

“…Model validation was conducted using quantitative polymerase chain reaction (qPCR) in an independent cohort of six children (three healthy, three diabetic).ResultsTranscriptomic analysis revealed significant differential expression patterns between pre-diabetic and control groups. Four model combinations demonstrated superior predictive performance: Lasso+K-Nearest Neighbors, Elastic Net + K-Nearest Neighbors, Elastic Net + Random Forest, and Support Vector Machine+K-Nearest Neighbors. …”
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Uncovering abnormal gray and white matter connectivity patterns in Alzheimer’s disease spectrum: a dynamic graph theory analysis for early detection by Juanjuan Jiang, Tao Kang, Ronghua Ling, Yingqian Liu, Jiuai Sun, Yiming Li, Xiaoou Li, Hui Yang, Bingcang Huang, the Alzheimer’s Disease Neuroimaging Initiative

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Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning by Zi-hang Li, Shi-pian Li, Shi-pian Li, Ya-hao Li, Yu-cheng Wang, Zhen-yu Tang, Kai-yang Xu, Xiao-rong Li, Zhen Tan, Jiao-yi Pan, Jin-tao Liu, Hong Jiang, Zhi-jia Ma, Yu-xiang Dai, Yu-xiang Dai, Yu-xiang Dai, Peng-fei Yu

“…IntroductionAging is characterized by gradual structural and functional changes in the body over time, with intervertebral disc degeneration (IVDD) representing a key manifestation of spinal aging and a major contributor to low back pain (LBP).MethodsThis study utilized bioinformatics and machine learning approaches to identify aging-related biomarkers associated with IVDD in whole blood samples. …”
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A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles by Yanping Hu, Bo Wang, Chao Shi, Pengfei Ren, Chengjuan Zhang, Zhizhong Wang, Jiuzhou Zhao, Jiawen Zheng, Tingjie Wang, Bing Wei, He Zhang, Rentao Yu, Yihang Shen, Jie Ma, Yongjun Guo

“…Patients from the three clusters had varied pathologies, MMR statuses and TIIC distribution patterns (p < .05). Kaplan–Meier’s analysis showed that the prognosis of C3 was inferior compared to C1 and C2 (p = .0025). …”
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From lab to field with machine learning – Bridging the gap for movement analysis in real-world environments: A commentary by Carlo Dindorf, Fabian Horst, Djordje Slijepčević, Bernhard Dumphart, Jonas Dully, Matthias Zeppelzauer, Brian Horsak, Michael Fröhlich

“…J., & Delp, S. L. (2018). Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. …”
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Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024 by Muhammad Junaid Akram, Muhammad Junaid Akram, Asad Nawaz, Asad Nawaz, Yuan Yuxing, Yuan Yuxing, Jinpeng Zhang, Jinpeng Zhang, Huang Haixin, Huang Haixin, Lingjuan Liu, Lingjuan Liu, Xu Qian, Jie Tian, Jie Tian

“…The study utilized the R- package bibliometrix-biblioshiny, VOSviewer, Scimago Graphica and CiteSpace to analyze the publications on cardiomyopathy, heart failure, machine learning, and artificial intelligence. Key metrics examined included top institutions, countries, journals, keywords, co-authorship networks, and keyword co-occurrence patterns. …”
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Mapping distribution patterns of invasive alien species in the Santa Monica Mountains using airborne imaging spectroscopy and line-point transect data by Nicoletta Stork, Amy Mainzer, Brooke Carruthers, Mark Mendelsohn, Gregory Asner, Roberta Martin

“…These results emphasize the value of fine-spatial and spectral resolution data for detecting IPS patterns in complex environments and demonstrate how combining airborne imaging spectroscopy with field-based methods can inform IPS management.…”
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Fault Diagnosis of Train Bogie Bearing Based on Multi-scale Sample Entropy Improved Extreme Learning Machine by JIN Zhenzhen, HE Deqiang, MIAO Jian, XU Weichang

“…Finally, the feature vector set is divided into test set and training set, and the improved extreme learning machine is used as a pattern recognition algorithm for fault pattern recognition. …”
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Characterising physical activity patterns in community-dwelling older adults using digital phenotyping: a 2-week observational study protocol by Bruno Bonnechère, Dominique Hansen, Kim Daniels, Sharona Vonck, Jolien Robijns, Annemie Spooren

“…All measurements align with the Behaviour Change Wheel framework, assessing capability, opportunity and motivation. Machine learning will analyse data, employing unsupervised learning (eg, hierarchical clustering) to identify PA behaviour patterns and supervised learning (eg, recurrent neural networks) to predict behavioural influences. …”
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Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning by Shakhawan Hares Wady

“…The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)).  …”
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Machine Learning-Driven Parametric Analysis of Eco-Friendly Ultrasonic Welding for AL6061-CU Alloy Joints by A. Karan, S. Arungalai Vendan, M. R. Nagaraj, M. Chaturvedi, S. Sivadharmaraj

“…In addition, the joint surface exhibits a consistent weld pattern in the majority of the samples, with just a few instances of inconsistencies when the trail is carried out at low heat input. …”
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CPG-Based Control of an Octopod Biomimetic Machine Lobster for Mining Applications: Design and Implementation in Challenging Underground Environments by Jianwei Zhao, Haokun Zhang, Mingsong Bao, Boxiang Yin, Yiteng Zhang, Zhen Jiang

“…Central pattern generators (CPGs) have been extensively researched and validated as a well-established methodology for bionic control, particularly within the field of legged robotics. …”
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Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis by Deepak Kumar, Brijesh Bakariya, Chaman Verma, Zoltán Illés

“…There is an urgent need for non-invasive methods to diagnose various stages of liver dysfunction and uncover hidden pattern based on individual disease characteristics. …”
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Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population by Concepción Violán, Quintí Foguet-Boreu, Sergio Fernández-Bertolín, Marina Guisado-Clavero, Margarita Cabrera-Bean, Francesc Formiga, Jose Maria Valderas, Albert Roso-Llorach

“…Objectives The aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. …”
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Application of Machine Learning Techniques for Predicting Heating Coil Performance in Building Heating Ventilation and Air Conditioning Systems by Adam Nassif, Pasidu Dharmasena, Nabil Nassif

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Comparison of Induction Machine Drive Control Schemes on the Distribution of Power Losses in a Three-Level NPC Converter by Carlos A. Reusser, Matías Parra, Gerardo Mino-Aguilar, Victor R. Gonzalez-Diaz

“…Multilevel converters, in particular the three-level neutral point clamped (3L-NPC) topology driving medium-voltage induction machines, has become the most commonly adopted solution. …”
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