Estimating comprehensive growth index for drip-irrigated spring maize in junggar basin via satellite imagery and machine learning by Mingjie Ma, Jinghua Zhao, Tingrui Yang, Feng Liu, Yingying Yuan, Shijiao Ma, Zikang Chang

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Machine learning based differential diagnosis of SAPHO syndrome and secondary bone tumors using whole body bone scintigraphy by Hongyang Jiang, Aihui Liu, Yihan Cao, Zhimin Lin, Haixu Jiang, Shengyan Liu, Qiuwei Peng, Xia Wu, Yuchen Liu, Xinbo Yu, Maming Wei, Yalin Pan, Chen Li, Zhenhua Ying

“…In this multicenter study, we aim to identify different bone and joint involvement patterns between the two disease entities through multiple methods to build machine-learning models and explore interpretable variables. …”
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Using explainable machine learning and eye-tracking for diagnosing autism spectrum and developmental language disorders in social attention tasks by Adoración Antolí, Adoración Antolí, Francisco Javier Rodriguez-Lozano, José Juan Cañas, Julia Vacas, Julia Vacas, Fátima Cuadrado, Fátima Cuadrado, Araceli Sánchez-Raya, Araceli Sánchez-Raya, Carolina Pérez-Dueñas, Carolina Pérez-Dueñas, Juan Carlos Gámez-Granados

“…BackgroundEye-tracking technology has proven to be a valuable tool in detecting visual scanning patterns associated with autism spectrum disorder (ASD). …”
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Toward automated plantar pressure analysis: machine learning-based segmentation and key point detection across multicenter data by Carlo Dindorf, Jonas Dully, Steven Simon, Dennis Perchthaler, Stephan Becker, Hannah Ehmann, Christian Diers, Christoph Garth, Michael Fröhlich

“…Plantar pressure analysis is a pivotal tool for assessing foot function, diagnosing deformities, and characterizing gait patterns. Traditional proportion-based segmentation methods are often limited, particularly for atypical foot structures and low-quality data. …”
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Disulfidptosis-related gene in acute myocardial infarction and its diagnostic value and functions based on bioinformatics analysis and machine learning by Liwen Chen, Jinru Wei, Jinru Wei, Guoxiong Deng, Guoxiong Deng, Guien Xu

“…The research delved into the expression patterns and molecular mechanisms of differentially expressed disulfidptosis related genes in AMI, offering a foundation for precise AMI diagnosis and the identification of novel therapeutic targets.…”
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Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis and machine... by Chenghui Cao, Chenghui Cao, Wenwu Liu, Xin Guo, Shuwei Weng, Yang Chen, Yonghong Luo, Shuai Wang, Botao Zhu, Botao Zhu, Yuxuan Liu, Yuxuan Liu, Daoquan Peng

“…Thus, we aimed to identify and validate efferocytosis linked signatures for detection of MASH.MethodsWe retrieved gene expression patterns of MASH from the GEO database and then focused on assessing the differential expression of efferocytosis-related genes (EFRGs) between MASH and control groups. …”
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Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach by Elizabeth P.V. Le, Mark Y.Z. Wong, Leonardo Rundo, Jason M. Tarkin, Nicholas R. Evans, Jonathan R. Weir-McCall, Mohammed M. Chowdhury, Patrick A. Coughlin, Holly Pavey, Fulvio Zaccagna, Chris Wall, Rouchelle Sriranjan, Andrej Corovic, Yuan Huang, Elizabeth A. Warburton, Evis Sala, Michael Roberts, Carola-Bibiane Schönlieb, James H.F. Rudd

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Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer by Haojie Dai, Zijie Yu, You Zhao, Ke Jiang, Zhenyu Hang, Xin Huang, Hongxiang Ma, Li Wang, Zihao Li, Ming Wu, Jun Fan, Weiping Luo, Chao Qin, Weiwen Zhou, Jun Nie

“…In addition, ANLN regulated immune infiltration patterns and was also inseparable from overall methylation levels. …”
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Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning by Bin He, Yan Cheng, Juan Wang, Ya Zhan, YanQun Liu

“…Cell trajectory analysis was carried out to explore the differentiation patterns of these fibroblasts using Monocle. Additionally, a cell-cell interaction analysis was performed using the CellChat package, and biological function and pathway enrichment analyses were done using GO, KEGG and GSEA pathways. …”
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A Similarity-Based Approach for Diagnosing the Aging of Lithium-Ion Batteries in Second Life Combining Time Series and Machine Learning by Daniela Galatro, Cristina H. Amon

“…In this work, we propose a similarity-based approach for diagnosing the aging of LiBs in their second life, which combines time series analysis and machine learning to help identify trends and patterns in the aging process. …”
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Prediction of instability of formwork concrete pier based on big data machine learning for secondary mining without coal pillar mining by Yanhui Zhu, Ye Tian, Peilin Gong, Kang Yi, Tong Zhao

“…Through field research, numerical simulation, theoretical analysis, big data machine learning, and field testing, the stress migration patterns and destabilization mechanisms of flexible formwork concrete pier columns under secondary mining conditions were investigated. …”
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Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches by Pei Gao, Ahmad Kamal Nasution, Naoaki Ono, Shigehiko Kanaya, Md. Altaf-Ul-Amin

“…The second approach leverages advanced machine learning techniques, particularly focusing on feature extraction and pattern recognition. …”
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Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis by Feifeng Jiang, Jun Ma

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Diagnostic performance of actigraphy in Alzheimer’s disease using a machine learning classifier – a cross-sectional memory clinic study by Mathias Holsey Gramkow, Andreas Brink-Kjær, Frederikke Kragh Clemmensen, Nikolai Sulkjær Sjælland, Gunhild Waldemar, Poul Jennum, Steen Gregers Hasselbalch, Kristian Steen Frederiksen

“…Abstract Background Movement patterns, activity levels and circadian rhythm are altered in Alzheimer’s disease (AD) and can be assessed by actigraphy using wearable sensors. …”
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Application of Machine Learning and Emerging Health Technologies in the Uptake of HIV Testing: Bibliometric Analysis of Studies Published From 2000 to 2024 by Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Lateef Babatunde Amusa, Hossana Twinomurinzi, Refilwe Nancy Phaswana-Mafuya

“…ObjectiveThe study aimed to examine the characteristics, citation patterns, and contents of published studies applying machine learning and emerging health technologies in HIV testing from 2000 to 2024. …”
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Measuring place rather than space: integrating landscape qualities for the urban–rural gradient using people’s perceptions and machine learning by Michal Switalski, Maarten Van Strien, Adrienne Grêt-Regamey

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Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence by Yasin Görmez, Fatma Hilal Yagin, Burak Yagin, Yalin Aygun, Hulusi Boke, Georgian Badicu, Matheus Santos De Sousa Fernandes, Abedalrhman Alkhateeb, Mahmood Basil A. Al-Rawi, Mohammadreza Aghaei, Mohammadreza Aghaei

“…ObjectivesThis study aims to build a machine learning (ML) prediction model integrated with explainable artificial intelligence (XAI) to categorize obesity levels from physical activity and dietary patterns. …”
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Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques by Wen Liu, Tiezhu Shi, Zhinian Zhao, Chao Yang

“…This approach enables the precise mapping of coastal salt-affected soil and vegetation patterns, addressing spatial heterogeneity and dynamic environmental conditions. …”
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Applications of resting-state fMRI and machine learning in cognitive impairment in type 2 diabetes mellitus: A scoping review by Zhizhong Sun, Zidong Cao, Limin Ge, Yifan Li, Haoming Huang, Mingrui Li, Shijun Qiu

“…Our analysis indicates that machine learning methods can effectively distinguish between T2DM patients with and without CI, revealing abnormal functional connectivity patterns linked to cognitive decline. …”
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Identification and experimental validation of biomarkers for chronic obstructive pulmonary disease complicated with pulmonary arterial hypertension based on bioinformatics and mach... by YANG Yan, TAO Chunrong, ZHU Youjun

“…Functional enrichment analysis indicated that DEGs in COPD were primarily associated with major histocompatibility complex (MHC) Ⅱ and cell division, and involved in lysosomes, oxidative phosphorylation, and cell cycle pathways (P<0.05). Machine learning identified 5 potential biomarkers (GRN, KLF4, SHTN1, LRP1, and GPNMB), and subsequent single-cell analysis revealed that these markers exhibited reverse expression patterns during disease progression. …”
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