Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery by Yingcan Li, Yu Shen, Yezi Cai, Yulin zhang, Jiahui Gao, Lei Huang, Weinuo Si, Kai Zhou, Shan Gao, Qichao Luo

“…Code and data are available at https://github.com/t9lex/Meta-DEP .…”
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Construction of a clinically significant prostate cancer risk prediction model based on traditional diagnostic methods by Wen-Tong Ji, Yong-Kun Wang, Zhan-Yang Han, Si-Qi Wang, Yao Wang, Yao Wang

“…Subsequently, we developed an online calculator (https://jiwentong0.shinyapps.io/dynnomapp/) with six variables for biopsy optimization.ConclusionThis study incorporated the results of three traditional diagnostic methods to establish a cost-effective and highly accurate model for predicting csPCa before biopsy. …”
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MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer by Jie Lan, Heng Wang, Jing Huang, Weiyi Li, Min Ao, Wanfeng Zhang, Junhao Mu, Li Yang, Longke Ran

“…Furthermore, we embedded this model in a web application called MoLPre (https://molpre.cqmu.edu.cn/), a user‐friendly tool assisting in the metastasis prediction of cT1 solid lung cancer.…”
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Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus by Qipeng Wei, Zihao Liu, Xiaofeng Chen, Hao Li, Weijun Guo, Qingyan Huang, Jinxiang Zhan, Shiji Chen, Dongling Cai, Dongling Cai

“…Eight supervised ML algorithms were applied to construct predictive models. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis (DCA), accuracy, sensitivity, specificity, and F1 score. …”
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Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors by Qianjin Lu, Ying Zhang, Cheng Zhao, Yu Lei, Hui Jin, Qilin Li

“…Leave-one-out cross-validation confirmed that the ForestMDG model had the best accuracy (0.8338). Finally, we developed a dynamic nomogram for practical use, which is accessible via the following link: https://yingzhang99321.shinyapps.io/dynnomapp/.Conclusion We created a user-friendly dynamic nomogram for predicting the relative risk of SLE onset based on occupational and living environmental exposures.Trial registration number ChiCTR2000038187.…”
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A neural network based computational model to predict the output power of different types of photovoltaic cells. by WenBo Xiao, Gina Nazario, HuaMing Wu, HuaMing Zhang, Feng Cheng

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Predictive modelling and ranking: Azadirachta indica compounds through indices and multi-criteria decision-making techniques by D. S. Anuradha, B. Jaganathan

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A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data. by Panagiotis Pergantas, Andreas Tsatsaris, Chrisovalantis Malesios, Georgia Kriparakou, Nikolaos Demiris, Yiannis Tselentis

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Development of a Predictive Model for Hepatitis B Virus (HBV) Status Using Gender and Serum Biomarkers by Stanley Obinna Ezeadichie, Joy E. Ikekpeazu, Amechi Uchenna Katchy, Mima Wariso, Chioma Cordelia Ugwu, Osah Martins Onwuka

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Application of multimodal large language models for safety indicator calculation and contraindication prediction in laser vision correction by Joon Yul Choi, Doo Eun Kim, Sung Jin Kim, Hannuy Choi, Tae Keun Yoo

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Clinical characteristics, outcomes, and predictive modeling of patients diagnosed with immune checkpoint inhibitor therapy-related pneumonitis by Antonious Hazim, Irene Riestra Guiance, Jacob Shreve, Gordon Ruan, Damian McGlothlin, Allison LeMahieu, Robert Haemmerle, Keith Mcconn, Richard C. Godby, Lisa Kottschade, Anna Schwecke, Casey Fazer-Posorske, Tobias Peikert, Eric Edell, Konstantinos Leventakos, Ashley Egan

“…The grading of pneumonitis was defined in accordance with ASCO guidelines (Schneider et al. in J Clin Oncol 39(36):4073–4126, 2021. https://doi.org/10.1200/JCO.21.01440 ). Predictive modeling was performed using gradient boosting machine learning technology, XGBoost (Chen in 1(4):1, 2015), to conduct binary classification and model reverse engineering using Shapley statistics (Lundberg and Lee in Adv Neural Inf Process Syst 30, 2017). …”
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Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots by Mehmet Sıddık Çadırcı, Musa Çadırcı

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PK/PD Modeling as a Tool for Predicting Bacterial Resistance to Antibiotics: Alternative Analyses of Experimental Data by M. V. Golikova, E. N. Strukova, Y. A. Portnoy, A. A. Firsov

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A multi-task learning model for global soil moisture prediction based on adaptive weight allocation by Yeguang li, Haiying Liu, Tianshan Lv

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A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction by Yelian Sun, Longkun Yu, Dandan Zhu

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rbpTransformer: A novel deep learning model for prediction of piRNA and mRNA bindings. by Ahmet Gürhanlı

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Predicting the Development of a Low Gas-Saturated Zone of the Medvezhye Field Based on Geological and Hydrodynamic Modeling by Artem A. Shirjaev, Kirill E. Vinogradov, Y. V. Vaganov, Vitaliya O. Naumenko

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Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis by Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

“…While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. …”
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Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks by Jonathan Schmitt, Abbas Razvi, Christoph Grapentin

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Using ARCH and GARCH mathematical models to predict the general budget deficit for the period from (2008) to (2023) by Saad Obaid Jameel AL-Masoodi

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