A Mathematical Model for Prediction the Embedment Depth of the Contiguous Piles Used in the Interchange of Zakho Entrance by Najdat S. Akrawi, Shimal A. Ahmed

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Correction: Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19

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Demographic Factors that Predict Open University Undergraduate Students' Well-Being by Marien Alet Graham, Ishmael Magare, Irma Eloff

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A Near-Real-Time Model for Predicting Electricity Disruptions in Texas During Winter Storms by Jangjae Lee, Sangkeun Lee, Supriya Chinthavali, Stephanie Paal

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Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle by Rachappa Jopate, Piyush Kumar Pareek, DivyaJyothi M. G, Ariam Saleh Zuwayid Juma Al Hasani

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Correction: Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19. by Aldo Córdova-Palomera, Csaba Siffel, Chris DeBoever, Emily Wong, Dorothée Diogo, Sandor Szalma

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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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Class-balanced negative training sets for improving classifier model predictions of enhancer-promoter interactions by Osamu Maruyama, Tsukasa Koga

“…Further advanced methods in generating negative EPIs should further improve prediction accuracy. The source code is available at https://github.com/maruyama-lab-design/CBOEP2 .…”
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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

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Model for Prediction of Testing Time of a Computer Program for Automated Reliability Evaluation of Semiconductor Devices by V. O. Kaziuchyts, S. M. Borovikov, E. N. Shneiderov

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Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction by Chenglong Guo, Binyu Gao, Xuexue Han, Tianxing Zhang, Tianqi Tao, Jinggang Xia, Honglei Liu

“…This study aimed to develop an interpretable artificial intelligence (AI) model for HF severity prediction using multidimensional clinical data. …”
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Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease by Ying He, Fan Lin, Xin Zheng, Qiaobin Chen, Meng Xiao, Xiaoting Lin, Hongbiao Huang

“…This study presents a region-specific, interpretable ML model for early IVIG resistance prediction in KD. …”
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Improving drug-drug interaction prediction via in-context learning and judging with large language models by He Qi, He Qi, Xiaoqiang Li, Chengcheng Zhang, Tianyi Zhao, Tianyi Zhao

“…To further refine predictions, we employ GPT-4 as a discriminator to assess the relevance of predictions generated by multiple LLMs.ResultsDDI-JUDGE achieves the best performance among all models in both zero-shot and few-shot settings, with an AUC of 0.642/0.788 and AUPR of 0.629/0.801, respectively. …”
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Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms by Liu　Yuxiao, Dong　Yanwu, Jiang　Zhouhua, Chen　Xi

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A comparative approach of machine learning models to predict attrition in a diabetes management program. by Samantha Kanny, Grisha Post, Patricia Carbajales-Dale, William Cummings, Janet Evatt, Windsor Westbrook Sherrill

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A hierarchical Bayesian model to predict APOE4 genotype and the age of Alzheimer's disease onset. by Francis Hane, Carolyn Augusta, Owen Bai

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XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults by Miguel A. Fernández-Blázquez, José M. Ruiz-Sánchez de León, Rubén Sanz-Blasco, Emilio Verche, Marina Ávila-Villanueva, María José Gil-Moreno, Mercedes Montenegro-Peña, Carmen Terrón, Cristina Fernández-García, Jaime Gómez-Ramírez

“…The aim of this study is to develop and validate machine learning (ML) models based on non-imaging features to predict the risk of MCI conversion in cognitively healthy older adults over a three-year period. …”
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Postpartum depression risk prediction using explainable machine learning algorithms by Xudong Huang, Lifeng Zhang, Chenyang Zhang, Jing Li, Chenyang Li

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Analysis of influencing factors and trend prediction based on quantile regression and GM(1, 1) model by Lianhui Li, Adham Manyara, Jie Liu

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AlphaBind, a domain-specific model to predict and optimize antibody–antigen binding affinity by Aditya A. Agarwal, James Harrang, David Noble, Kerry L. McGowan, Adrian W. Lange, Emily Engelhart, Miranda C. Lahman, Jeffrey Adamo, Xin Yu, Oliver Serang, Kyle J. Minch, Kimberly Y. Wellman, David A. Younger, Randolph M. Lopez, Ryan O. Emerson

“…Recent advances in deep learning provide opportunities to address this challenge by learning sequence–function relationships to accurately predict fitness landscapes. These models enable efficient in silico prescreening and optimization of antibody candidates. …”
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