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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Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

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Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information by Sungyeol Lee, Jaemo Kang, Jinyoung Kim

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

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Liberating the open and distance learning model from the chains of oppressive education theories by Mackenzie Ishmael Chibambo, Joseph Jinja Divala, Aggripa Chingombe

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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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A Larval “Recruitment Kernel” to Predict Hatching Locations and Quantify Recruitment Patterns by Wei Shi, Leon Boegman, Shiliang Shan, Yingming Zhao, Josef D. Ackerman, Zachary Amidon, Aidin Jabbari, Edward Roseman

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Toward a Multi‐Representational Approach to Prediction and Understanding, in Support of Discovery in Hydrology by Luis A. De la Fuente, Hoshin V. Gupta, Laura E. Condon

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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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Performance Prediction of the Gearbox Elastic Support Structure Based on Multi-Task Learning by Chengshun Zhu, Zhizhou Lu, Jie Qi, Meng Xiang, Shilong Yuan, Hui Zhang

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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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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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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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Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma by Guangliang Duan, Qi Huo, Wei Ni, Fei Ding, Yuefang Ye, Tingting Tang, Huiping Dai

“…Subsequently, four survival machine learning models were developed to predict LUSC prognosis. These models were validated in the testing sets and integrated into an online tool to assist in survival prediction. …”
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