Biomarker and clinical data–based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal st... by Eva María Arriero-País, María Auxiliadora Bajo-Rubio, Roberto Arrojo-García, Pilar Sandoval, Guadalupe Tirma González-Mateo, Patricia Albar-Vizcaíno, Gloria del Peso-Gilsanz, Marta Ossorio-González, Pedro Majano, Manuel López-Cabrera, Gloria del Peso-Gilsanz

“…Linear discriminant analysis (LDA) discerns among transfer to haemodialysis or death, predicts whether the cause of PD end is ultrafiltration failure (UFF) or cardiovascular disease (CVD) and anticipates the type of CVD (receiver operating characteristic curve under the area>0.71).Discussion Our combination of longitudinal PD datasets, attribute shrinkage and gold-standard algorithms with overfitting testing and class imbalance ensures robust predictions in PD. …”
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A statistical and machine learning approach for monthly precipitation forecasting in an Amazon city by Ewerton Cristhian Lima de Oliveira, Eduardo Costa de Carvalho, Edmir dos Santos Jesus, Rafael de Lima Rocha, Rafael de Lima Rocha, Helder Moreira Arruda, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Renata Gonçalves Tedeschi

“…Besides the use of algorithms, another evaluation was conducted on Feature Composition based on statistical methods to investigate the impact of variables on the prediction.ResultsThe results obtained in our investigation indicate that the vector autoregressive moving average with exogenous regressors (VARMAX) model achieved the best performance in rainfall forecasting, with an average root mean square error (RMSE) of 9.1833 in time series cross-validation, outperforming the other models.DiscussionThe climate-driven patterns directly influenced the performance of the rainfall forecasting models evaluated in this study. …”
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Machine Learning in Maritime Safety for Autonomous Shipping: A Bibliometric Review and Future Trends by Jie Xue, Peijie Yang, Qianbing Li, Yuanming Song, P. H. A. J. M. van Gelder, Eleonora Papadimitriou, Hao Hu

“…Future research will concentrate on three main areas: evolving safety objectives towards proactive management and autonomous coordination, developing advanced safety technologies, such as bio-inspired sensors, quantum machine learning, and self-healing systems, and enhancing decision-making with machine learning algorithms such as generative adversarial networks (GANs), hierarchical reinforcement learning (HRL), and federated learning. …”
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Prediction of canopy mean traits in herbaceous plants by the UAV multispectral data: The quest for a better leaf-to-canopy upscaling method by Yuanqi Shan, Yunlong Yao, Lei Wang, Zhihui Wang, Huaihu Yi, Yi Fu, Weineng Li, Xuguang Zhang, Wenji Wang, Zhongwei Jing

“…This study proposed a novel approach for calculating canopy mean traits using the geometric mean method and compared its performance to that of the CWM methods in combination with three modeling algorithms Partial Least Squares Regression (PLSR), Random Forest regression (RF), and Support Vector Machine regression (SVM). …”
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Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

“…It outlines the evolution of computational chemistry and the transformative role of AI in interpreting complex molecular data, automating feature extraction, and improving decision-making across the drug development pipeline. Core AI algorithms support vector machines, random forests, graph neural networks, and transformers are examined for their applications in molecular representation, virtual screening, and ADMET property prediction. …”
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Assessment and Modeling of Green Roof System Hydrological Effectiveness in Runoff Control: A Case Study in Dublin by Mehdi Gholamnia, Payam Sajadi, Salman Khan, Srikanta Sannigrahi, Saman Ghaffarian, Himan Shahabi, Francesco Pilla

“…The comprehensive dataset enabled detailed modeling of runoff hydrograph parameters using rainfall hyetographs, which were subsequently analyzed through sophisticated machine learning algorithms. This research introduces an innovative approach by identifying the optimal combination of variables for modeling key runoff characteristics, including Water Retention Amount (WRA), Total RUnoff Volume (TRUV), Peak Runoff Discharge (PRD), and Peak Flow Reduction (PFR). …”
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Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting by Agbassou Guenoukpati, Akuété Pierre Agbessi, Adekunlé Akim Salami, Yawo Amen Bakpo

“…This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. …”
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Changes in soil pH on the Tibetan Plateau from the 1980 s to 2020 s by Lirong Zhao, Bo Pang, Jiangtao Hong, Xinxin Zhang, Jinmei Li, Xiaodan Wang

“…Here, we utilized resampling methods and machine learning algorithms to explore the spatiotemporal variations and driving factors of soil pH on the Tibetan Plateau from the 1980 s to 2020 s. …”
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Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning by Yating Zhan, Min Weng, Yangyang Guo, Dingfeng Lv, Feng Zhao, Zejun Yan, Junhui Jiang, Yanyi Xiao, Lili Yao

“…Integrative machine learning combination based on 10 machine learning algorithms was used for the construction of robust signature. …”
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Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches by Haiyang Zhang, Shuo Wu, Lehan Yang, Chengjing Fan, Huifang Chen, Hui Wang, Tianyi Zhu, Yinwei Li, Jing Sun, Xuefei Song, Huifang Zhou, Terry J Smith, Xianqun Fan

“…The distribution of GO-QOL scores was analyzed, and linear regression and machine learning algorithms were utilized. Results: The median QOL-VF and QOL-AP scores were 64.29 and 62.5, respectively. …”
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Type 1 diabetes prevention clinical trial simulator: Case reports of model‐informed drug development tool by Juan Francisco Morales, Marian Klose, Yannick Hoffert, Jagdeep T. Podichetty, Jackson Burton, Stephan Schmidt, Klaus Romero, Inish O'Doherty, Frank Martin, Martha Campbell‐Thompson, Michael J. Haller, Mark A. Atkinson, Sarah Kim

“…To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. …”
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Electrophysiological changes in the acute phase after deep brain stimulation surgery by Lucia K. Feldmann, Diogo Coutinho Soriano, Jeroen Habets, Valentina D'Onofrio, Jonathan Kaplan, Varvara Mathiopoulou, Katharina Faust, Gerd-Helge Schneider, Doreen Gruber, Georg Ebersbach, Hayriye Cagnan, Andrea A. Kühn

“…These findings are important for the timing of electrophysiology-supported DBS programming, such as contact selection or adaptive algorithms.…”
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Analysis of human brain RNA-seq data reveals combined effects of 4 types of RNA modifications and 18 types of programmed cell death on Alzheimer’s disease by Ke Ye, Xinyu Han, Mengjie Tian, Lulu Liu, Xu Gao, Qing Xia, Dayong Wang

“…Finally, by combining unsupervised consensus clustering, gene co-expression networks, and machine learning algorithms, an RNA modification-related programmed cell death network was constructed, and the pivotal roles of programmed cell death genes in key modules were identified. …”
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Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction by Zhengbiao Hu, Shuangshan Bu, Kai Wang, Qianqian Cao, Huanhuan Zheng, Jie Yang, Shanshan Chen, Yuemeng Wu, Wenkai Ren, Chenlei He

“…In this study, multiple machine learning algorithms were used to develop a non-invasive and improved model for the diagnosis of IgAN. …”
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The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository: rationale and blueprint by Hakim Baazaoui, Stefan T. Engelter, Stefan T. Engelter, Stefan T. Engelter, Henrik Gensicke, Henrik Gensicke, Henrik Gensicke, Lukas S. Enz, Lukas S. Enz, Marios Psychogios, Marios Psychogios, Matthias Mutke, Matthias Mutke, Patrik Michel, Davide Strambo, Alexander Salerno, Henk A. Marquering, Paul J. Nederkoorn, Nabila Wali, Stephanie Tanadini-Lang, Björn Menze, Ezequiel de la Rosa, Kaiyuan Yang, Gian Marco De Marchis, Gian Marco De Marchis, Tolga D. Dittrich, Tolga D. Dittrich, Francesco Valletta, Francesco Valletta, Manon Germann, Carlo W. Cereda, João Pedro Marto, Lisa Herzog, Patrick Hirschi, Zsolt Kulcsar, Susanne Wegener, Susanne Wegener

“…There, patient identifiers are removed and images are algorithmically de-faced. De-identified structured clinical data are connected to the imaging data by a new identifier. …”
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Identification of GABBR2 as a diagnostic marker and its association with Aβ in Alzheimer's disease by Huijun Li, Yawei Fan, Chan Chen, Yuzhong Xu, Xiong Wang, Wei Liu

“…The overlapped hub genes were further processed using machine learning algorithms, intersected with module gene from protein-protein interaction (PPI) network constructed with DEGs, to yield co-hub genes. …”
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Enhancing Crop Type Mapping in Data-Scarce Regions Through Transfer Learning: A Case Study of the Hexi Corridor by Jingjing Mai, Qisheng Feng, Shuai Fu, Ruijing Wang, Shuhui Zhang, Ruoqi Zhang, Tiangang Liang

“…High-confidence pixels from the United States Cropland Data Layer (CDL), along with high-density time series data derived from Sentinel-1, Sentinel-2, and Landsat-8 satellite imagery, as well as key vegetation indices, were selected as training samples for the source domain. Various algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and TrAdaBoost, were employed to transfer knowledge from the source domain to the target domain for crop type mapping. …”
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Discovery of a novel binding pocket in PPARγ for partial agonists: structure-based virtual screening identifies ginsenoside Rg5 as a partial agonist promoting beige adipogenesis by Zhen Wang, Kexin Shui, Zehui Zhang, Yihan Chen, Nanfei Yang, Shiliang Ji, Pingping Shen, Qiang Tian, Qiang Tian

“…Here, we employed six computational algorithms (Fpocket, DeepSite, CavityPlus, DoGSiteScorer, CASTpFold, POCASA) to identify a novel allosteric pocket (pocket 6–5) in the PPARγ ligand-binding domain (LBD), localized at the helix 3 (H3), helix 2 (H2), helix 2'(H2′), and β-sheet interface. …”
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Ensemble learning techniques reveals multidimensional EEG feature alterations in pediatric schizophrenia by Ying Mao, Ying Mao, Fang Wang, Shan Wang, Zhaowei Wang, Gang Li, Xuchen Qi, Xuchen Qi, Yu Sun, Yu Sun

“…In summary, this study not only showcases the potential of advanced ensemble learning algorithms in precisely identifying pediatric SCZ, but also provides new insights into the altered brain functions in pediatric SCZ patients, which may benefit the future development of automatic diagnosis systems.…”
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Abnormal eye movement, brain regional homogeneity in schizophrenia and clinical high-risk individuals and their associated gene expression profiles by Zhaobin Chen, Yangpan Ou, Yudan Ding, Ying Wang, Huabing Li, Feng Liu, Ping Li, Dongsheng Lv, Yong Liu, Bing Lang, Jingping Zhao, Wenbin Guo

“…Twenty-seven drug-naïve FSZ, 25 CHR, and 28 healthy controls (HCs) were recruited for eye-tracking tasks and resting-state functional magnetic resonance imaging to evaluate eye movement and regional homogeneity (ReHo) differences. Machine-learning algorithms were used to differentiate FSZ from CHR. …”
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