Discriminating the origin of fish from closely related water bodies by combining NMR spectroscopy with statistical analysis and machine learning by Stefan Kuhn, Kärt Reitel, Elmina Homapour, Kärolin Kork, Väino Vaino, Timo Arula, Priit Bernotas, Indrek Reile

“…Statistical methods including principal component analysis (PCA) and linear discriminant analysis (LDA) are typically applied to NMR data to correlate spectra with a particular research question.Herein we examine fish from three closely related water bodies and demonstrate that reliable determination of the water body that a particular fish originates from by traditional statistical analysis (PCA and LDA) of fish NMR spectra is not possible. …”
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Exploring the risk factors and clustering patterns of periodontitis in patients with different subtypes of diabetes through machine learning and cluster analysis by Anna Zhao, Yuxiang Chen, Haoran Yang, Tingting Chen, Xianqi Rao, Ziliang Li

“…Conclusions: Machine learning combined with consensus clustering analysis revealed a greater prevalence of periodontitis among patients with diabetes mellitus in Cluster B. …”
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Comparison of an Emergency Medicine Asynchronous Learning Platform Usage Before and During the COVID-19 Pandemic: Retrospective Analysis Study by Blake Briggs, Madhuri Mulekar, Hannah Morales, Iltifat Husain

“…Website traffic and podcast analytics were studied monthly from 2 time periods of 20 months each, before the pandemic (July 11, 2018, to February 31, 2020) and during the pandemic (May 1, 2020, to December 31, 2021). March and April 2020 data were omitted from the analysis due to variations in closure at various domestic and international locations. …”
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Data for peace: how novel data sources and technology can enhance peace? by Innar Liiv, Stefaan Verhulst, Evelyne Tauchnitz, Michele Giovanardi, Kalypso Nicolaïdis, Martin Wählisch

“…In particular, the articles of the collection illustrate how advanced techniques—including machine learning, network analysis, specialised text classifiers, and large-scale predictive analytics—can deepen our understanding of conflict dynamics by revealing subtle interdependencies and patterns. …”
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Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer’s disease: transcriptomics combined with machine learning analysis by Meiling Wang, Aojie He, Yubing Kang, Zhaojun Wang, Yahui He, Kahleong Lim, Chengwu Zhang, Li Lu

“…Finally, combining bulk RNA sequencing data and two machine learning algorithms (least absolute shrinkage and selection operator and random forest), four characteristic Alzheimer’s disease feature genes were identified: somatostatin (SST), protein tyrosine phosphatase non-receptor type 3 (PTPN3), glutinase (GL3), and tropomyosin 3 (PTM3). …”
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Temporal and Machine Learning-Based Principal Component and Clustering Analysis of VOCs and Their Role in Urban Air Pollution and Ozone Formation by Balendra V. S. Chauhan, Maureen J. Berg, Ajit Sharma, Kirsty L. Smallbone, Kevin P. Wyche

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Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis by Mehrsa Moannaei, Faezeh Jadidian, Tahereh Doustmohammadi, Amir Mohammad Kiapasha, Romina Bayani, Mohammadreza Rahmani, Mohammad Reza Jahanbazy, Fereshteh Sohrabivafa, Mahsa Asadi Anar, Amin Magsudy, Seyyed Kiarash Sadat Rafiei, Yaser khakpour

“…This study aimed to evaluate the performance and limitations of machine learning and deep learning algorithms in detecting diabetic retinopathy. …”
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Enhancing socio-emotional learning and mental health through computational thinking: a cross-cultural analysis of the COMPUSEL programme by Adelinda Candeias, Adriana Felix, Anisoara Dumitrache, Beatrice Almășan, Ilke Evin Gencel, Magdalena Zadworna, Karolina Kossakowska, Angeliki Sakellariou, Miltos Sakellariou, Cavus Sahin, Cigdem Sahin Taskin

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Identification of matrix stiffness-related molecular subtypes in HCC via integrating multi-omics analysis and machine learning algorithms by Hanqi Li, Jiayi Zhang, Yu Shi, Huanhuan Wang, Ruida Yang, Shaobo Wu, Yue Li, Xue Yang, Qingguang Liu, Liankang Sun

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Predicting indoor temperature of solar green house by machine learning algorithms: A comparative analysis and a practical approach by Wenhe Liu, Tao Han, Cong Wang, Feng Zhang, Zhanyang Xu

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Machine Learning-Enhanced Discrimination of Gamma-Ray and Hadron Events Using Temporal Features: An ASTRI Mini-Array Analysis by Valentina La Parola, Giancarlo Cusumano, Saverio Lombardi, Antonio Alessio Compagnino, Antonino La Barbera, Antonio Tutone, Antonio Pagliaro

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Machine learning driven multi-omics analysis of the genetic mechanisms behind the double-coat fleece formation in Hetian sheep by Yanwei Zhang, Wenrong Li, Xinming Xu, Mengwan Xie, Liping Tang, Peiyu Zheng, Nannan Song, Lijuan Yu, Jiang Di

“…Enrichment analyses revealed these genes were primarily involved in pathways related to wool growth and energy metabolism. PPI network analysis and machine learning identified IRF2BP2 and EGFR as key functional genes associated with coat fleece type.DiscussionThis study enhances understanding of the genetic mechanisms governing double-coated fleece formation in Hetian sheep. …”
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Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis by Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Behnaz Niroomand, Nasira Faizi, Pooya Eini, Mohammad Amin Habibi, Alireza Mohseni, Mohammadmahdi Sabahi, Abdulrahman Albakr, Michael Karsy, Hamid Borghei-Razavi

“…Abstract Background Predicting pituitary adenoma (PA) recurrence after surgical resection is critical for guiding clinical decision-making, and machine learning (ML) based models show great promise in improving the accuracy of these predictions. …”
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π‐PhenoDrug: A Comprehensive Deep Learning‐Based Pipeline for Phenotypic Drug Screening in High‐Content Analysis by Xiao Li, Qinxue Ouyang, Mingfei Han, Xiaoqing Liu, Fuchu He, Yunping Zhu, Ling Leng, Jie Ma

“…These results confirm that π‐PhenoDrug can achieve high‐throughput and accuracy analysis of cell phenotypic data using an unbiased and automated workflow, improving drug discovery efficiency.…”
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Analysis of Reading Comprehension to Solve Problems on "Descriptive Task" of Madrasah Ibtidaiyah Students by Agus Qowiyudin, Hulliyatus Salma, Rina Hidayati

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iMLGAM: Integrated Machine Learning and Genetic Algorithm‐driven Multiomics analysis for pan‐cancer immunotherapy response prediction by Bicheng Ye, Jun Fan, Lei Xue, Yu Zhuang, Peng Luo, Aimin Jiang, Jiaheng Xie, Qifan Li, Xiaoqing Liang, Jiaxiong Tan, Songyun Zhao, Wenhang Zhou, Chuanli Ren, Haoran Lin, Pengpeng Zhang

“…Abstract To address the substantial variability in immune checkpoint blockade (ICB) therapy effectiveness, we developed an innovative R package called integrated Machine Learning and Genetic Algorithm‐driven Multiomics analysis (iMLGAM), which establishes a comprehensive scoring system for predicting treatment outcomes through advanced multi‐omics data integration. …”
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Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts by ChuXia Tan, Yuan Liu, Lijun Li, Ying Li, Pingting Yang, Yinglong Duan, Xingxing Wang, Huiyi Zhang, Jingying Wang, Honglian Zhang

“…Sociodemographic and health behavior data were collected through electronic questionnaires. …”
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Effectiveness of Project-Based Learning and Think-Pair-Share Cooperative Learning Models in Improving Students’ Learning Outcomes Based on Their Learning Interest by Grace Fine Situngkir, Saliman Saliman

“…Data analysis was conducted using an independent t-test, gain score test, and simple linear regression with the assistance of SPSS. …”
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Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration by Maoqiang Lin, Maoqiang Lin, Shaolong Li, Yabin Wang, Yabin Wang, Guan Zheng, Guan Zheng, Fukang Hu, Fukang Hu, Qiang Zhang, Qiang Zhang, Pengjie Song, Haiyu Zhou, Haiyu Zhou

“…The receiver operating characteristic (ROC) curve, nomogram, and Decision Curve Analysis (DCA) were used to evaluate the model effect. …”
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Improving Diagnostic Performance for Head and Neck Tumors with Simple Diffusion Kurtosis Imaging and Machine Learning Bi-Parameter Analysis by Suzuka Yoshida, Masahiro Kuroda, Yoshihide Nakamura, Yuka Fukumura, Yuki Nakamitsu, Wlla E. Al-Hammad, Kazuhiro Kuroda, Yudai Shimizu, Yoshinori Tanabe, Masataka Oita, Irfan Sugianto, Majd Barham, Nouha Tekiki, Nurul N. Kamaruddin, Miki Hisatomi, Yoshinobu Yanagi, Junichi Asaumi

“…<b>Conclusions:</b> The usefulness of bi-parameter analysis with ML methods for the differential diagnosis of benign and malignant head and neck tumors using SDI data were demonstrated.…”
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