Integrative analysis of RNA-Seq data and machine learning approaches to identify Biomarkers for Rhizoctonia solani resistance in sugar beet by Bahman Panahi, Mahdi Hassani, Nahid Hosseinzaeh Gharajeh

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The Enterprise School Readiness Prediction System (ESRPS) Uses Machine Learning to Assess Children's Readiness for Entering Elementary School by Muhammad Choerul Umam, Cicilia Dyah Sulistyaningrum I., Dydik Kurniawan, Priyono Tri Febrianto

“…The system also demonstrated the feasibility of practical deployment for educational use. The study concludes that ESRPS effectively uses machine learning to assess school readiness, highlighting the value of data preprocessing and model tuning in enhancing accuracy. …”
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Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study by Jiexin Chen, Qiongbing Zheng, Youmian Lan, Meijing Li, Ling Lin

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Machine Reading Comprehension for the Tamil Language With Translated SQuAD by Anton Vijeevaraj Ann Sinthusha, Eugene Y. A. Charles, Ruvan Weerasinghe

“…This superior performance is attributed to the model’s cross-lingual learning capability and the increased number of data records used for fine-tuning. …”
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Using optimized dimensionality reduction and machine learning to explain driving processes of phytoplankton community assembly in large mountain rivers by Jingxu Ye, Daikui Li, Qi Liu, Jianying Song, Jiawei Song, Zhigang Zu, Yujun Yi

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Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials by Jordan McBreen, Md Ali Babar, Diego Jarquin, Yiannis Ampatzidis, Naeem Khan, Sudip Kunwar, Janam Prabhat Acharya, Samuel Adewale, Gina Brown-Guedira

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Machine‐Learning Based Multi‐Layer Soil Moisture Forecasts—An Application Case Study of the Montana 2017 Flash Drought by J. Du, J. S. Kimball, K. Jencso, Z. Hoylman, C. Brust, D. Ketchum, Y. Xu, H. Lu, J. Sheffield

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Global Burden of Alzheimer’s Disease Attributable to High Fasting Plasma Glucose: Epidemiological Trends and Machine Learning Insights by Ma Y, Huang S, Dong Y, Jin Q

“…Machine learning models are effective tools for identifying individuals at high risk of elevated blood glucose leading to cognitive impairment.Keywords: epidemiology, diabetes, cognitive decline, disability-adjusted life years, machine learning, public health…”
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Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis by Rodrigo Yáñez-Sepúlveda, Aldo Vásquez-Bonilla, Rodrigo Olivares, Pablo Olivares, Juan Pablo Zavala-Crichton, Claudio Hinojosa-Torres, Catalina Muñoz-Strale, Frano Giakoni-Ramírez, Josivaldo de Souza-Lima, Jacqueline Páez-Herrera, Jorge Olivares-Arancibia, Tomás Reyes-Amigo, Guillermo Cortés-Roco, Juan Hurtado-Almonacid, Eduardo Guzmán-Muñoz, Nicole Aguilera-Martínez, José Francisco López-Gil, Boryi A. Becerra-Patiño, Juan David Paucar-Uribe, Exal Garcia-Carrillo, Vicente Javier Clemente-Suárez

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Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey by Manhui Zhang, Xian Xia, Qiqi Wang, Yue Pan, Guanyi Zhang, Zhigang Wang

“…We tested and evaluated the performance of four traditional machine learning algorithms commonly used in epidemiological studies: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, and two deep learning algorithms: TabNet and AMFormer model. …”
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Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks by Chalita Jainonthee, Phutsadee Sanwisate, Panneepa Sivapirunthep, Chanporn Chaosap, Raktham Mektrirat, Sudarat Chadsuthi, Veerasak Punyapornwithaya

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Advanced machine learning framework for thyroid cancer epidemiology in Iran through integration of environmental socioeconomic and health system predictors by Mohsen Soleimani, Hossein Chiti

“…This study addresses this critical gap by employing an advanced multi-model machine learning (ML) framework to elucidate the spatiotemporal determinants of TC incidence across Iran’s 31 provinces, offering novel insights to inform evidence-based public health strategies. …”
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Identification and validation of shared biomarkers and drug repurposing in psoriasis and Crohn’s disease: integrating bioinformatics, machine learning, and experimental approaches... by Xiaolong Li, Xiaolong Li, Hui Cao, Mutian Niu, Mutian Niu, Qingbo Liu, Bin Liang, Junfeng Hou, Junfeng Hou, Jian Tu, Jian Tu, Jintao Gao, Jintao Gao

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Crop Statistic to Annual Map: Tracking spatiotemporal dynamics of crop-specific areas through machine learning and statistics disaggregating by Xiyu Li, Le Yu, Zhenrong Du, Xiaoxuan Liu

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Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts by Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park

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Accurate and robust prediction of Amyloid-β brain deposition from plasma biomarkers and clinical information using machine learning by Jiayuan Xu, Andrew J. Doig, Sofia Michopoulou, Sofia Michopoulou, Petroula Proitsi, Petroula Proitsi, Fumie Costen, The Alzheimer's disease neuroimaging initiative

“…Brain Aβ status was determined using plasma biomarkers [Aβ42, Aβ40, Phosphorylated tau (pTau) 181, Neurofilament light chain (NfL)], Apolipoprotein E (APOE) genotype, and clinical information [Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), age, education year, and gender]. Decision tree, random forest, support vector machine, and multilayer perceptron machine learning methods were used to combine all this information. …”
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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

“…This study aims to reveal the risk factors of HUA in healthy physical examination populations of different age groups and construct a machine learning-driven risk prediction model to achieve precise intervention. …”
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Multimodal Computational Approach for Forecasting Cardiovascular Aging Based on Immune and Clinical–Biochemical Parameters by Madina Suleimenova, Kuat Abzaliyev, Ainur Manapova, Madina Mansurova, Symbat Abzaliyeva, Saule Doskozhayeva, Akbota Bugibayeva, Almagul Kurmanova, Diana Sundetova, Merey Abdykassymova, Ulzhas Sagalbayeva

“…Based on the clinical, biochemical and immunological data obtained, a model for predicting the risk of premature cardiovascular aging was developed using mathematical modelling and machine learning methods. …”
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Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning by Paul Sanmartin Mendoza, Margarita Castillo Ramirez, Ste`phanie Galvis Castro, Vera Santiago Martinez, Alexander Elias Parody Muñoz

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GIS, remote sensing, and machine learning for identifying optimal locations for seawater intake and brine disposal in coastal desalination systems by Radwa Marzouk, Tareq M. Alkhaldi, Sakthivelu U, Saranya A, Arutperumjothi G, Srinivasan S

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