A Novel Local Binary Patterns-Based Approach and Proposed CNN Model to Diagnose Breast Cancer by Analyzing Histopathology Images by Mehmet Gul

“…This article proposed two methods, one CNN-based and the other local binary pattern (LBP)-based, to perform the preliminary diagnosis process on breast cancer histopathology images with high performance. …”
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Synergistic use of satellite, legacy, and in situ data to predict spatio-temporal patterns of the invasive Lantana camara in a savannah ecosystem by Lilly Theresa Schell, Emma Evers, Sarah Schönbrodt-Stitt, Konstantin Müller, Maximilian Merzdorf, Drew Arthur Bantlin, Insa Otte

“…Additionally, the red edge, shortwave, and near-infrared spectral bands were identified as essential predictors, highlighting the efficacy of combining remote sensing and anthropogenic data with machine learning techniques to predict invasive species distributions. …”
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ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering by Maryam Shahabikargar, Amin Beheshti, Wathiq Mansoor, Xuyun Zhang, Eu Jin Foo, Alireza Jolfaei, Ambreen Hanif, Nasrin Shabani

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Longitudinal transcriptome analysis reveals distinct gene expression patterns in traditional Chinese medicine syndromes of upper respiratory tract infections by Hao Zou, Hao Zou, Changrui Huang, Changrui Huang, Qinqi Feng, Qinqi Feng, Bang Yu, Bang Yu, Jian Liu, Xinyang Shu, Xiaolu Nie, Hongchun Zhang, Xiaohui Zou

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Construction and Application of Carbon Emissions Estimation Model for China Based on Gradient Boosting Algorithm by Dongjie Guan, Yitong Shi, Lilei Zhou, Xusen Zhu, Demei Zhao, Guochuan Peng, Xiujuan He

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Quantitative assessment of factors that influence heat vulnerability in residential areas using machine learning and unmanned aerial vehicle by Jawoon Gu, Dongwoo Kim, Chulmin Jun, Seungwoo Son

“…High-resolution thermal imagery captured by unmanned aerial vehicles (UAVs) and interpretable machine learning (ML) techniques were used to model and analyze thermal patterns at the microscale. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

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Mathematical Modeling as a Aspect for Designing Agricultural Machines and Units (Development History Of Southern Urals Scientific School) by Yu. S. Tsench, E. V. Godlevskaya

“…(Research purpose) The objective of the study is to identify the patterns in designing tillage machines and units and, based on these patterns forecast their potential enhancements using mathematical modeling. …”
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Synergistic bioinformatics and sophisticated machine learning unveil ferroptosis-driven regulatory pathways and immunotherapy potential in breast carcinoma by Lei Xia, Zhen Ye, Man Zheng, Zhaofeng Tan

“…Conclusions The integration of bioinformatics and machine learning in this study underscores a strong correlation between FRG expression patterns and BRCA prognosis, affirming their potential as precise biomarkers for personalized immunotherapy.…”
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Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision by Chenglong Fan, Guanglin Yang, Cheng Li, Jiwen Cheng, Shaohua Chen, Hua Mi

“…The hub genes associated with DN and glycolysis-related clusters were identified via weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. Finally, the expression patterns of these hub genes were validated using single-cell sequencing data and quantitative real-time polymerase chain reaction (qRT-PCR). …”
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Street View-Enabled Explainable Machine Learning for Spatial Optimization of Non-Motorized Transportation-Oriented Urban Design by Yichen Ruan, Xiaoyi Zhang, Shaohua Wang, Xiuxiu Chen, Qiuxiao Chen

“…To advance evidence-based urban design prioritizing non-motorized mobility, this study proposes a street view-enabled explainable machine learning framework that systematically links built environment semantics to non-motorized transportation vitality optimization. …”
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Synergizing human insight and machine learning: A dual-lens approach to uncovering healthcare research and innovation outcomes by Stijn Horck, Sanne Steens, Jermain Kaminski

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Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis by Alireza Shahin-Shamsabadi, John Cappuccitti

“…Typically, proteomics research focuses narrowly on using a limited number of datasets, hindering cross-study comparisons, a problem that can potentially be addressed by machine learning. Despite this potential, machine learning has seen limited adoption in the field of proteomics. …”
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Understanding household VMT generation: A comparative analysis with traditional statistical models and a machine-learning approach by Guang Tian, Bob Danton, Bin Li, Vijaya Gopu, Julius A. Codjoe

“…This study is the first to compare these methods’ indications of the impacts of land-use patterns on VMT generation using a large multiregional dataset. …”
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Integrating bioinformatics and machine learning to elucidate the role of protein glycosylation-related genes in the pathogenesis of diabetic kidney disease. by Ziyang Liu, Zengyuan Qin, Wenxin Bai, Shasha Wang, Chunling Huang, Na Li, Lei Yan, Yue Gu, Fengmin Shao

“…<h4>Results</h4>Unsupervised clustering of glycosylation-related genes revealed two distinct DKD molecular subtypes with differential pathway activation (e.g., extracellular matrix remodeling) and immune infiltration patterns. Six hub genes (S100A12, EXT1, SBSPON, ADAMTS1, FMOD, SPTB) were identified as critical to DKD pathogenesis through machine learning. …”
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Unveiling the role of coagulation-related genes in acute myeloid leukemia prognosis and immune microenvironment through machine learning by Liyun Ji, Yanxia Yang, Siyue Ma

“…This study aims to investigate the expression patterns of coagulation-related genes in AML and their clinical relevance. …”
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Advancing Hydrogel-Based 3D Cell Culture Systems: Histological Image Analysis and AI-Driven Filament Characterization by Lucio Assis Araujo Neto, Alessandra Maia Freire, Luciano Paulino Silva

“…<b>Background:</b> Machine learning is used to analyze images by training algorithms on data to recognize patterns and identify objects, with applications in various fields, such as medicine, security, and automation. …”
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Identifying Antibiotic-Resistant Mutants in β-Lactamases for Class A and Class B Using Unsupervised Machine Learning by Soumya Lipsa Rath, Smaranika Mohapatra, Veena Gayathri

“…The current study incorporates the techniques of machine learning to cluster the patterns of the proteins which may be antibiotic resistant. …”
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Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus by Hongwei Zhou, Xiaoqing Li, Yanyu Zhang, Feng Wei, Zhiyu Liu, Yan Zhao, Xubo Zhuang, Xia Liu, Haizhou Zhou

“…Correlation analysis underscored strong relationships among key genes, OS/AOS levels, and vital metabolites.ConclusionThis multi-omics and machine learning–based investigation uncovered major disruptions in OS-related metabolic pathways and metabolites in SLE, ultimately identifying six key genes with distinct expression patterns across immune cell subsets. …”
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Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations by Jie Yang, Fanyan Ou, Binbin Li, Lixiong Zeng, Qiuli Chen, Houyu Gan, Jianing Yu, Qian Guo, Jihua Feng, Jianfeng Zhang

“…This study, through the integrated application of computational biology and machine learning algorithms, discovered biomarkers of PCD patterns that affect cytokine storm-mediated inflammation and immunosuppressive effects in sepsis populations across different age groups (neonates, children, and adults). …”
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