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

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Combining multi-surface and biotoxicity models to predict cadmium bioavailability and accumulation in a soil collembolan by Simin Li, Jiawen Zhou, Tingting Mu, Tuozheng Wu, Zhu Li, Xin Ke, Longhua Wu, Yongming Luo, Yuanqing Bu

“…MSMs-rBLM model may be a new tool for the prediction of Cd ecological risks and bioaccumulation in soils.…”
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Risk prediction models for dental caries in children and adolescents: a systematic review and meta-analysis by Peng Zhang, Kang Li, Xijia Wang, Huifei Lu, Dandan Luo, Dunhui Yang, Shuqi Qiu, Haotao Zeng, Xianhai Zeng

“…The search focused on caries prediction models in children and adolescents.Eligibility criteria Eligible studies included observational research (cohort, case–control and cross-sectional designs) that developed risk prediction models for dental caries in children and adolescents aged ≤18 years. …”
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Comparison Airport Traffic Prediction Performance Using BiGRU and CNN-BiGRU Models by Willy Riyadi, Jasmir, Xaverius Sika

“…COVID-19 pandemic has significantly disrupted the aviation industry, highlighting the critical need for accurate airport traffic predictions. This study compares the performance of BiGRU and CNN-BiGRU models to enhance airport traffic forecasting accuracy models from March to December 2020. …”
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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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Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models by Zhilong Song, Shuaihua Lu, Minggang Ju, Qionghua Zhou, Jinlan Wang

“…Leveraging CSLLM, tens of thousands of synthesizable theoretical structures are successfully identified, with their 23 key properties predicted using accurate graph neural network models.…”
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Enhancing Concrete Workability Prediction Through Ensemble Learning Models: Emphasis on Slump and Material Factors by Jiangsong Jiang, Chunhong Xin, Sifei Wu, Wenbing Chen, Hui Li, Zhaolun Ran

“…This study advances concrete workability prediction by integrating ensemble learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), and gradient boosted regression trees (GBRTs), and XGBoost showing superior accuracy. …”
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Novel integrative models to predict the severity of inflammation and fibrosis in patients with drug-induced liver injury by Yue Zhang, Chuan Lu, Jingying Xu, Qiqi Ma, Mei Han, Li Ying

“…Then, backward stepwise regression, best subset and logistic regression models were conducted for predicting significant liver inflammation and fibrosis. …”
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EXAMINING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE AND DEEP LEARNING MODELS FOR THE PREDICTION OF COVID-19 by Hafiza Zoya Mojahid, Jasni Mohamad Zain, Marina Yusoff, Abdul Basit, Abdul Kadir Jumaat, Mushtaq Ali

Subjects:

Prediction of failures in the project management knowledge areas using optimized ensemble models in software companies by Lamia Berriche, Abderrazak Loulizi

“…CatBoost demonstrated the best accuracy, achieving 94.02%, and demonstrated the best generalization. These models showed strong performance in predicting failures related to scope and cost management but were less accurate when predicting failures in human resource management. …”
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A comparative study of various statistical and machine learning models for predicting restaurant demand in Bangladesh by Md Shakhawath Hossain, Farjana Parvin

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The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy by Mingyan Zhang, Junfa Sheng

“…This study aimed to develop and validate a predictive model that integrates radiomic features derived from super-resolution (SR) ultrasound images with clinical biomarkers to improve risk stratification. …”
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Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

“…This requires strong predictive models to tease apart what we can do to detect and introduce early-prevention. …”
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Prediction of the need for maintenance of rigid pavements using finite element models and artificial neural networks by Lorena Jacqueline Chamorro Chamorro, Elisa Dominguez Sotelino

“…The developed rigid pavement management system uses the Artificial Neural Networks (ANN) technique for the prediction of both pavement response to fatigue accumulation and the behavior of the modeled pavement with a high degree of precision. …”
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High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions. by Phaedra Agius, Aaron Arvey, William Chang, William Stafford Noble, Christina Leslie

“…Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. …”
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Analyzing and predicting global happiness index via integrated multilayer clustering and machine learning models by Boxu Yang, Xiang Xie

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Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

Subjects: “…Short-term temperature prediction…”
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Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors by Deepa Tilwani, Deepa Tilwani, Deepa Tilwani, Deepa Tilwani, Christian O'Reilly, Christian O'Reilly, Christian O'Reilly, Christian O'Reilly, Nicholas Riccardi, Valerie L. Shalin, Valerie L. Shalin, Dirk-Bart den Ouden, Julius Fridriksson, Svetlana V. Shinkareva, Svetlana V. Shinkareva, Amit P. Sheth, Amit P. Sheth, Amit P. Sheth, Rutvik H. Desai, Rutvik H. Desai

“…To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. …”
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General models for predicting the liquid thermal conductivity of fatty acid esters based on smart methods by Chou-Yi Hsu, Ahmad Mohsin, Ramdevsinh Jhala, Nagaraj Patil, Debasish Shit, V.K. Bupesh Raja, Manoj Kumar Ojha, Abinash Mahapatro, Deepak Gupta, Fereydoon Ranjbar

“…A comparison with literature correlations showed that the novel models offer significant improvements in the thermal conductivity prediction. …”
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Smooth predictions for age-period-cohort models: a comparison between splines and random process by Connor Gascoigne, Andrea Riebler, Theresa Smith

“…Abstract Background Age-Period-Cohort (APC) models are well used in the context of modelling health and demographic data to produce smooth predictions of each time trend. …”
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