Predicting the future impact of climate change on the distribution of species in Egypt’s mediterranean ecosystems by Ahmed R. Mahmoud, Emad A. Farahat, Loutfy M. Hassan, Marwa Waseem A. Halmy

“…—under two GCMs of HadGEM3-GC31-LL and IPSL-CM6A-LR for the periods of 2060s and 2080s and two Shared Socioeconomic Pathway (SSP 1-2.6 and SSP5-8.5), comparing the use of MaxEnt and ensemble modelling techniques in predicting the impact of future climatic changes on these species’ distribution. …”
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Predicting climate-driven shift of the East Mediterranean endemic Cynara cornigera Lindl by Heba Bedair, Heba Bedair, Yehia Hazzazi, Asmaa Abo Hatab, Marwa Waseem A. Halmy, Mohammed A. Dakhil, Mohammed A. Dakhil, Mubaraka S. Alghariani, Mubaraka S. Alghariani, Mari Sumayli, A. El-Shabasy, Mohamed M. El-Khalafy

“…In fact, it is distributed in 3 fragmented locations in Egypt (Wadi Hashem (5 individuals), Wadi Um Rakham (20 individuals), Burg El-Arab (4 individuals)).MethodsIn this study, we examined C. cornigera’s response to predicted climate change over the next few decades (2020-2040 and 2061-2080) using species distribution models (SDMs). …”
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In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning by Ehsan Chatraei Azizabadi, Mohamed El-Shetehy, Xiaodong Cheng, Ali Youssef, Nasem Badreldin

“…This study evaluated the performance of three machine learning (ML) models—Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Regression (GBR)—for predicting potato N status and examined the impact of feature selection techniques, including Partial Least Squares Regression (PLSR), Boruta, and Recursive Feature Elimination (RFE). …”
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Early Prediction of Stroke Risk Using Machine Learning Approaches and Imbalanced Data by Hassan Qassim

“…Specifically, Decision Tree, Naïve Bayes, K- Nearest Neighbor (KNN) and Linear discriminant Analyses (LDA) models were trained on 11 attributes collected from 5110 patients to predict stroke risk. …”
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An Approach to, and Tool for, Predicting the Time Course of an Infectious Disease Outbreak as a Function of Behavioral Interventions by Abebe HT, Siraj A, Berhane K, Siraj D, Van Breukelen GJ

“…However, most decisions rely on expert opinions rather than robust epidemic model outputs. Implementing epidemic models can be challenging without a strong background in statistical modeling.Methods: This paper presents a simple, user-friendly tool in MATLAB to predict the time course of an infectious disease outbreak using various modified epidemic models. …”
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Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data by Andrea Angarita-Rodríguez, Andrea Angarita-Rodríguez, Andrea Angarita-Rodríguez, Nicolás Mendoza-Mejía, Nicolás Mendoza-Mejía, Janneth González, Jason Papin, Jason Papin, Jason Papin, Andrés Felipe Aristizábal, Andrés Pinzón

“…This method facilitates the reconstruction of context-specific models grounded in multi-omics data, enhancing their biological relevance and predictive capacity.ResultsUsing this approach, we successfully reconstructed an astrocyte GEM with improved prediction capabilities compared to state-of-the-art models available in the literature.DiscussionThese advancements underscore the potential of multi-omic inte-gration to refine metabolic modeling and its critical role in studying neurodegeneration and developing effective therapies.…”
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Perspectives on the Use of Transthoracic Echocardiography Results for the Prediction of Ventricular Tachyarrhythmias in Patients with Non-ischemic Cardiomyopathy by N. N. Ilov, D. R. Stompel, S. A. Boytsov, O. V. Palnikova, A. A. Nechepurenko

“…The metrics of the best predictive model were: AUC – 0.71 0.069 with 95% CI 0.574-0.843; specificity 50%, sensitivity 90.9%; diagnostic efficiency 57.1%.Conclusion. …”
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Baird´s tapir: predicting patterns of crop damage surrounding the Calakmul Biosphere Reserve, Campeche, Mexico by Isabel Serrano-Mac-Gregor, Rafael Reyna-Hurtado, Dolores Molina-Rosales, Eduardo Naranjo-Piñera

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Construction and validation of a risk prediction model for postoperative frailty in older adults:a multicenter study by Shilong Lu, Hao Liang, Jiamin Fang, Rui Chen, Huilian Liao, Mingming Xu, Yumei Chen, Huijin Sun, Lijuan Dong, Yingui Guo, Zhixia Jiang, Hui Xiao, Lin Wei

Subjects: “…Predictive model…”
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Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma by Liang Zheng, Wei Nie, Shuyuan Wang, Ling Yang, Fang Hu, Fang Hu, Meili Ma, Lei Cheng, Jun Lu, Bo Zhang, Jianlin Xu, Ying Li, Yinchen Shen, Wei Zhang, Runbo Zhong, Tianqing Chu, Baohui Han, Xiaoxuan Zheng, Xiaoxuan Zheng, Hua Zhong, Xueyan Zhang

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Development of a predictive model for pneumothorax after microwave ablation based on radiomics and clinical baseline data by Xiangyu Xie, Kun Li, Lei Chen, Hong Li, Chaofan Meng, Liang Zheng

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Construction of prediction model for water retention of forest ecosystem in alpine region based on vegetation spectral features by Teng Niu, Zhongze Hou, Jiaxin Yu, Jie Lu, Qiang Yu, Linzhe Yang, Jun Ma, Yafei Liu, Hui Shi, Xuyang Jin

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Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms by Yonghan Luo, Wenrui Ding, Xiaotao Yang, Houxi Bai, Feng Jiao, Yan Guo, Ting Zhang, Xiu Zou, Yanchun Wang

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DNA methylation expression patterns predict outcome of clear cell renal cell carcinoma by Xuwen Li, Haoxi Wang, Yajian Li, Yihao Zhu, Yabo Zhai, Nianzeng Xing, Xiongjun Ye, Feiya Yang

“…Differential analysis, univariate Cox regression, and LASSO regression were used to find survival—related CpG sites and build a risk score model. The model was evaluated by the area under the curve, and multivariate analysis determined risk factors. …”
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Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability by Yan Jiang, Hong-wei Wang, Fang-ying Tian, Yue Guo, Xiu-mei Wang

“…This study aims to identify the risk factors of CRPA infection and construct a machine learning model to provide a prediction tool for clinical prevention and control. …”
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Investigating the Impact of Climate Change on the Effective Indicators in Desertification and Predicting its Spatial Changes by Azam Sadat Hosseini Khezr Abad, Abassali Vali, Amirhossein Halabian, Mohammad Hossein Mokhtari, Seyyed Ali Mousavi

“…Also, based on the IMDPA model, 80.54 percent of the area of ​​the region is in the severe desertification class in the base period. …”
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Prediction of Monochorionic Diamniotic Twin Pregnancy Outcomes Based on Early Second Trimester Ultrasound by Slavica Aksam, Jelena Dotlic, Dusica Kocijancic Belovic, Mirjana Marjanovic Cvjeticanin, Ivana Vukovic, Milena Mitrovic, Ivana Babovic, Srboljub Milicevic, Snezana Plesinac, Snezana Buzadzic

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Bayesian predictive modelling to ascertain factors affecting cattle milk production in Tanzania: Evidence from the national panel surveys 2012 – 2021 by Zainabu Bonza, Rosalia Katapa, Amina Msengwa

“…This study aimed to evaluate and compare Bayesian predictive models to identify and quantify the key household inputs affecting cattle milk production in Tanzania. …”
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Forecasting and Comparison of Mechanical Properties of Wooden Structure Models by Darius Albrektas, Ernestas Ivanauskas, Ugne Misiunaite

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Quantitative Systems Pharmacology Model to Predict Target Occupancy by Bruton Tyrosine Kinase Inhibitors in Patients With B‐Cell Malignancies by Oleg Demin Jr, Ying Ou, Galina Kolesova, Dmitry Shchelokov, Alexander Stepanov, Veronika Musatova, Sri Sahasranaman, Yating Zhao, Xiangyu Liu, Zhiyu Tang, William D. Hanley

“…Consistent with observed clinical data, the model predicted that zanubrutinib 160 mg twice daily resulted in higher median trough BTK occupancy in PBMCs, LNs, and BM compared with ibrutinib and acalabrutinib. …”
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