An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects by Shreya Mishra, Neetesh Pandey, Atul Rawat, Divyanshu Srivastava, Arjun Ray, Vibhor Kumar

“…Such abstract representations of protein structures hand in hand with amino-acid features can be used for different purposes, such as modelling the biophysical property of proteins. Our method outperformed graph-Fourier and convolutional neural-network-based methods in predicting the biophysical properties of proteins. …”
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Establishment of a nine‐gene prognostic model for predicting overall survival of patients with endometrial carcinoma by Jianchao Ying, Qian Wang, Teng Xu, Jianxin Lyu

“…Therefore, our goal is to build a robust prognostic model for predicting overall survival (OS) in EC patients. …”
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Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction by Trevor Winger, Cagri Ozdemir, Shanti L. Narasimhan, Jaideep Srivastava

“…<b>Results:</b> With the progressive introduction of patient-specific data, the model demonstrated significant improvements in predictive capabilities. …”
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Random Displacement Method-based Model for Predicting the Distribution of Net Sediment Deposition in Vegetated Channels by Chuan LI, Sichen SUN, Yuqi SHAN, Yonghao LIU, Chao LIU

“…In addition, a model is proposed to predict the distribution of suspended sediment deposition, specifically applicable within emergent vegetation canopies with real plant morphology (reeds). …”
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Evaluating the Accuracy of the ERA5 Model in Predicting Wind Speeds Across Coastal and Offshore Regions by Mohamad Alkhalidi, Abdullah Al-Dabbous, Shoug Al-Dabbous, Dalal Alzaid

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Perspective: How complex in vitro models are addressing the challenges of predicting drug-induced liver injury by K. Taylor, R. Ram, R. Ram, L. Ewart, C. Goldring, G. Russomanno, G. P. Aithal, T. Kostrzewski, C. Bauch, J. M. Wilkinson, J. M. Wilkinson, S. Modi, J. G. Kenna, J. G. Kenna, J. Bailey, J. Bailey

“…Predicting which drugs might have the potential to cause drug-induced liver injury (DILI) is highly complex and the current methods, 2D cell-based models and animal tests, are not sensitive enough to prevent some costly failures in clinical trials or to avoid all patient safety concerns for DILI post-market. …”
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Modeling the significance of unified theory of acceptance and use of technology in predicting the intention and usage of eCNY by Yue Ma, Abdullah Al Mamun, Mohammad Masukujjaman, Roslan Ja’afar

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Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates by Neda Kaboodvand, Martijn P. van den Heuvel, Peter Fransson

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Validation of three models (Tolcher, Levine, and Burke) for predicting term cesarean section in Chinese population by Fangcan Sun, Minhong Shen, Bing Han, Youguo Chen, Fangfang Wu

“…Background: Some models predicting cesarean section (CS) have been proposed, with Tolcher, Levine, and Burke model well acknowledged. …”
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Predicting and Controlling Multiple Transmissions of Rotavirus Using Computational Biomedical Model in Smart Health Infrastructures by Titus Ifeanyi Chinebu, Kennedy Chinedu Okafor, Omowunmi Mary Longe, Kelvin Anoh, Henrietta Onyinye Uzoeto, Victor Onukwube Apeh, Ijeoma Peace Okafor, Bamidele Adebisi, Chukwunenye Anthony Okoronkwo

“…This paper proposes a biomedical model for predictive control of the virus spread based on Susceptible, Breastfeeding, Vaccinated, Infected, and Recovered (SBVIR) parameters. …”
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Development and Validation of a Diagnostic Nomogram Model for Predicting Cognitive Frailty in Acute Coronary Syndrome by Wang S, Sun Y, Tang W, Lu S, Feng F, Hou X, Ma L, Li R, Hu J, Liu B, Xing Y

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Fractional Order Mathematical Model for Predicting and Controlling Dengue Fever Spread Based on Awareness Dynamics by Ahmed S. Rashed, Mahy M. Mahdy, Samah M. Mabrouk, Rasha Saleh

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Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review by Farkhondeh Asadi, Milad Rahimi, Nahid Ramezanghorbani, Sohrab Almasi

“…Commonly used algorithms for survival prediction included random forest, support vector machines, logistic regression, XGBoost, and various deep learning models. …”
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Predicting Mineral N Release during Decomposition of Organic Wastes in Soil by Use of the SOILN_NO Model by Trine A. Sogn, Lars Egil Haugen

“…In order to predict the mineral N release associated with the use of organic waste as fertilizer in agricultural plant production, the adequacy of the SOILN_NO model has been evaluated. …”
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Predicting ecotopes from hydrodynamic model data: Towards an ecological assessment of nature-based solutions by Soesja Brunink, Gijs G. Hendrickx

“…This study introduces EMMA (Ecotope-Map Maker for Abiotics), a method for quantifying the effects of human interventions or climate change scenarios on estuarine ecosystems by linking abiotic characteristics derived from a hydrodynamic model to ecotopes. The Western Scheldt, an estuary connecting the Scheldt river to the North Sea in the Netherlands, serves as a case study. …”
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Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators by Joko Siswanto, Danny Manongga, Irwan Sembiring, Sutarto Wijono

“…The proposed prediction model performs predictions 12 months later for 4 predictions simultaneously with predicted fluctuations occurring simultaneously. …”
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Predicting postoperative nausea and vomiting using machine learning: a model development and validation study by Maxim Glebov, Teddy Lazebnik, Maksim Katsin, Boris Orkin, Haim Berkenstadt, Svetlana Bunimovich-Mendrazitsky

“…Therefore, prognostic models for the prediction of early and delayed PONV were developed in this study to achieve satisfactory predictive performance. …”
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Predicting Noise and User Distances from Spectrum Sensing Signals Using Transformer and Regression Models by Myke Valadão, Diego Amoedo, André Costa, Celso Carvalho, Waldir Sabino

“…This paper proposes a method for predicting noise levels and distances based on spectrum sensing signals using regression machine learning models. …”
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Predicting Marshall stability and flow parameters in asphalt pavements using explainable machine-learning models by Ibrahim Asi, Yusra I. Alhadidi, Taqwa I. Alhadidi

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Predicting suitable habitats and conservation areas for Suaeda salsa using MaxEnt and Marxan models by Yongji Wang, Zhusong Liu, Kefan Wu, Jiamin Peng, Yanyue Mao, Guanghua Zhao, Fenguo Zhang

“…Using 130 occurrence records and 14 selected environmental variables, this study applied the MaxEnt model to predict suitable habitats of S. salsa across China under current and future climate scenarios. …”
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