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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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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Muscle strength: development of predictive equation for handgrip strength in older adults on the primary health care by Maria Luiza Amorim Sena Pereira, Marília Conceição de Souza Caceres, Marlus Henrique Queiroz Pereira, Carolina Cunha de Oliveira, Bruno Klécius Andrade Teles, Elizabete Regina Araújo de Oliveira

“…This study aimed to develop equations for predicting HGS in community-dwelling older adults in Primary Health Care (PHC). …”
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Review and External Evaluation of Population Pharmacokinetic Models for Vedolizumab in Patients with Inflammatory Bowel Disease: Assessing Predictive Performance and Clinical Applicability by Marija Jovanović, Ana Homšek, Srđan Marković, Đorđe Kralj, Petar Svorcan, Tamara Knežević Ivanovski, Olga Odanović, Katarina Vučićević

“…The predictive performance of the models was assessed using prediction- and simulation-based diagnostics. …”
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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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Constructing a prediction model for acute pancreatitis severity based on liquid neural network by Jie Cao, Shike Long, Huan Liu, Fu’an Chen, Shiwei Liang, Haicheng Fang, Ying Liu

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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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Machine learning in predicting immediate and long-term outcomes of myocardial revascularization: a systematic review by B. I. Geltser, V. Yu. Rublev, M. M. Tsivanyuk, K. I. Shakhgeldyan

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Synthetic fibrosis distributions for data augmentation in predicting atrial fibrillation ablation outcomes: an in silico study by Alexander M. Zolotarev, Alexander M. Zolotarev, Kiane Johnson, Kiane Johnson, Yusuf Mohammad, Omnia Alwazzan, Omnia Alwazzan, Gregory Slabaugh, Gregory Slabaugh, Caroline H. Roney, Caroline H. Roney

“…We incorporated them into 1,000 bi-atrial meshes derived from a statistical shape model and simulated AF episodes on them before and after various ablation strategies to expand the training dataset for DL-based outcome prediction. …”
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Predicting Range Shifts of Five <i>Alnus</i> (Betulaceae) Species in China Under Future Climate Scenarios by Wenjie Yang, Zhilong Huang, Chenlong Fu, Zhuang Zhao, Xiaoyue Yang, Quanjun Hu, Zefu Wang

“…This study employed the MaxEnt model to predict the current and potential future suitable habitats of five <i>Alnus</i> species in China under four Shared Socioeconomic Pathways. …”
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Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them by Atsushi Danjo, Chiaki Kuwada, Reona Aijima, Asana Kamohara, Motoki Fukuda, Yoshiko Ariji, Eiichiro Ariji, Yoshio Yamashita

“…Two oral surgeons (a specialist and a clinical resident) predicted the difficulty level of the test data. This study also aimed to evaluate the performance of a deep learning model in predicting the necessity for tooth separation or bone removal during wisdom tooth extraction. …”
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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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Predictive models of reading difficulties considering neuropsycholinguistic profiles of atypical and ADHD-inattentive type readers, and eye-tracking measures by Norberto Pereira, Maria Armanda Costa, Manuela Guerreiro

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Predicting contrast sensitivity functions with digital twins by Yukai Zhao, Luis Andres Lesmes, Michael Dorr, Zhong-Lin Lu

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The application of multiple linear regression methods to FTIR spectra of fingernails for predicting gender and age of human subjects by L. Mihaly Cozmuta

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Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients by Madison Bush, Scott Jones, Catriona Hargrave

“…Due to the limited sample size further training of the predictive models including dMRI anatomy is recommended.…”
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Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings by David Cabezuelo, Izar Lopez-Ramirez, June Urkizu, Ander Goikoetxea

Subjects: “…power consumption prediction…”
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Investigating Enhanced Cooling Load Estimation through Hybrid LSSVR Models by Ali Hassan, Hamza Rashid

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