Supervised and unsupervised machine learning approaches for prediction and geographical discrimination of Iranian saffron ecotypes based on flower-related and phytochemical attribu... by Seid Mohammad Alavi-Siney, Jalal Saba, Alireza Fotuhi Siahpirani, Jaber Nasiri

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OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction by Fangfang Jian, Haihua Cai, Qushuo Chen, Xiaoyong Pan, Weiwei Feng, Ye Yuan, Ye Yuan

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Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress by Jin-Biao Xie, Shi-Jie Huang, Tian-Bao Yang, Wu Wang, Bo-Yang Chen, Lianyi Guo

“…These machine-learning strategies play a key role in data mining and pattern recognition tasks. …”
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Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study by Yuan-Hsin Chen, Ching-Hsuan Lin, Chiao-Hsin Fan, An Jim Long, Jeremiah Scholl, Yen-Pin Kao, Usman Iqbal, Yu-Chuan Jack Li

“…We developed an adapted Association Outlier Pattern (AOP) ML model to identify uncommon procedure-diagnosis combinations, specifically targeting discrepancies in laterality. …”
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PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data by Mousa Moradi, Saber Kazeminasab Hashemabad, Daniel M. Vu, Allison R. Soneru, Asahi Fujita, Mengyu Wang, Tobias Elze, Mohammad Eslami, Nazlee Zebardast

“…Two multi-layer perceptron (MLP) models were trained using 52 total deviation (TD) and pattern deviation (PD) values from Humphrey field analyzer (HFA) 24-2 VF tests, along with four clinical variables (age, gender, follow-up time, and race) to extract model weights. …”
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Blood from septic patients with necrotising soft tissue infection treated with hyperbaric oxygen reveal different gene expression patterns compared to standard treatment by Julie Vinkel, Alfonso Buil, Ole Hyldegaard

“…We aimed to identify gene expression patterns associated with effects of HBO2 treatment in patients with sepsis caused by NSTI, and to explore sepsis-NSTI profiles that are more receptive to HBO2 treatment. …”
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Abstract visual reasoning based on algebraic methods by Mingyang Zheng, Weibing Wan, Zhijun Fang

“…Abstract visual reasoning involves identifying abstract patterns embedded within composite images, considered a core competency of machine intelligence. …”
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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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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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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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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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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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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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A missing data processing method for dam deformation monitoring data using spatiotemporal clustering and support vector machine model by Yan-tao Zhu, Chong-shi Gu, Mihai A. Diaconeasa

“…It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring. …”
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Research on Employee Innovation Ability in Human–Machine Collaborative Work Scenarios—Based on the Grounded Theory Construct of Chinese Innovative Enterprises by Baorong Guo, Xiaoning Liu, Shuai Liao, Jiayi Hu

“…Furthermore, we develop a theoretical model elucidating the formation mechanism of employees’ innovative capabilities in human–machine collaboration contexts, identifying four core dimensions—innovation drivers, human–AI collaboration patterns, knowledge conversion pathways, and technological breakthroughs—that dominantly shape these capabilities. …”
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Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabol... by Pedro H. Godoy Sanches, Nicolly Clemente de Melo, Andreia M. Porcari, Lucas Miguel de Carvalho

“…We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. …”
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Balancing ethics and statistics: machine learning facilitates highly accurate classification of mice according to their trait anxiety with reduced sample sizes by Johannes Miedema, Beat Lutz, Susanne Gerber, Irina Kovlyagina, Hristo Todorov

“…Nevertheless, inter-individual variability and sex-specific patterns have been long disregarded in preclinical studies of anxiety and stress disorders. …”
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SEMP-TA: A Novel Stock Market Prediction Approach Based on Stacking Ensemble Machine Learning for Effective Trend Analysis by Chanda Raj Kumar, S. Manikandan

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