ImmuProgML: machine learning-based dissection of cancer-immune dynamics during tumor progression to improve immunotherapy by Hanxiao Zhou, Lan Mei, Qianyi Lu, Yakun Zhang, Yue Sun, Caiyu Zhang, Han Jiang, Jiajun Zhou, Xia Li, Yunpeng Zhang, Shangwei Ning

“…We introduced the DNEX score, which combines expression changes with immunotherapy-driven network topologies, and employed machine learning algorithms for prognostic and immunotherapy response predictions. …”
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Predicting the Risk of Preterm Birth Throughout Pregnancy Based on a Novel Transcriptomic Signature by Yang Pan, Yuxin Ran, Dongni Huang, Nanlin Yin, Yanqing Wen, Yan Jiang, Yamin Liu, Hongbo Qi

“…Differential analysis was performed using the DEseq2 and limma algorithms. Results. Circulatory IL6 signaling activity increased significantly in cases with preterm labor than in those with term pregnancies (normalized enrichment score (NES) = 1.857, P = 0.001). …”
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Global Sensitivity Analysis of CASC2D Hydrological Model Parameters by LI Xiao-ying, ZHANG Jin-hui, ZHAO Hong-jie

“…Furthermore, given the current lack of research on automated calibration for this model, the findings offer strategic guidance for the development of automated calibration algorithms.…”
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MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems by Abdul Jabbar, Marco Cocconcelli, Gianluca D’Elia, Davide Borghi, Luca Capelli, Jacopo Cavalaglio Camargo Molano, Matteo Strozzi, Riccardo Rubini

“…By addressing this gap, this work provides a critical resource for benchmarking and developing novel algorithms for fault diagnosis, signal processing, and machine learning in industrial transport applications. …”
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Human and Machine: A View through Kant's Use of Regulative Reason by Ümit Taştan

“…Since we can conceptualize a unity such as a formal system, we can discuss algorithms that operate according to this system. The regulative use of reason, which enables the establishment of such wholes, creates a qualitative difference between AI and the human mind when combined with the human’s intuitive thinking ability. …”
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Exploration of Predictive Models for Lean Services in Government Lean Banking Industry by Fazel Saydi, Morteza Musa Khani, Sedigheh Tootian, Shahrazad Tayaran

“…The path analysis of the discovered model was also examined using variance-based structural equation modeling algorithms with SPSS version 24 and SmartPLS version 3. …”
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Predictive Modelling of nephrotoxicity from vancomycin and piperacillin-tazobactam (VPT) combination and vancomycin as monotherapy: an artificial intelligence (AI) Approach by Dr Winnie Lee, Mr William J Bolton, Mr Richard C Wilson, Professor Pantelis Georgiou, Professor Alison H Holmes, Dr Nina Zhu, Dr Timothy M Rawson

“…Neural network and decision tree models will also be built to assess accuracy of prediction across these machine learning algorithms. From utilising the same methods, the probability of developing AKI from increased dosage of VPT will also be assessed. …”
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Reducing the Effects of City Heat Islands Through Cool Pavement Materials (Case Study: District 8 of Tabriz Metropolis) by Asghar Abedini, Mojtaba Azmoun, Kiarash Azarkish, Sina Moshtaghi

“…The objectives of this research included extracting, locating and analyzing the phenomenon of urban heat islands in the study area by using specific algorithms and preparing relevant maps and checking with the type of materials used, identifying two hot heat islands in the center and west with a temperature of 33.48 degrees Celsius and one island It followed a cold temperature in the north of the region with a temperature of 20.02 degrees Celsius. …”
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Interpretable machine learning analysis of immunoinflammatory biomarkers for predicting CHD among NAFLD patients by Wenyuan Dong, Hongcheng Jiang, Yu Li, Luo Lv, Yuxin Gong, Bao Li, Hongjie Wang, Hesong Zeng

“…Finally, three machine learning algorithms (RF, SVM and GLM) were used to identify significant risk factors. …”
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Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases ac... by Fuxu Wang, Yu Guo, Yuru Tang, Shuangmei Zhao, Kaige Xuan, Zhi Mao, Ruogu Lu, Rongyao Hou, Xiaoyan Zhu

“…Associations with mortality were analyzed using Kaplan-Meier(KM) curves, Cox proportional hazards models, restricted cubic splines (RCS), receiver operating characteristic (ROC) curves, landmark analyses, and subgroup analyses. Five ML algorithms were employed for mortality risk prediction, with SHapley Additive exPlanations (SHAP) applied to identify critical predictors. …”
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Forested Natura 2000 sites under climate change: effects of tree species distribution shifts by A. Reichmuth, A. Reichmuth, I. Kühn, I. Kühn, I. Kühn, A. Schmidt, A. Schmidt, D. Doktor, D. Doktor, D. Doktor

“…<i>Quercus</i> sp. reveal similar results, with <span class="inline-formula">23</span> <span class="inline-formula">%</span> absolute mean change (<span class="inline-formula">−23</span> <span class="inline-formula">%</span> loss, <span class="inline-formula">24</span> <span class="inline-formula">%</span> gain) at Natura 2000 sites, whereas <i>Fagus</i> remains stable throughout the model results with <span class="inline-formula">8</span> <span class="inline-formula">%</span> absolute mean change (<span class="inline-formula">−7</span> <span class="inline-formula">%</span> loss, <span class="inline-formula">9</span> <span class="inline-formula">%</span> gain). The best model algorithms for all species were the generalised additive models (GAMs). …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
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Comprehensive Analysis of Programmed Cell Death-Related Genes in Diagnosis and Synovitis During Osteoarthritis Development: Based on Bulk and Single-Cell RNA Sequencing Data by Zhou J, Jiao S, Huang J, Dai T, Xu Y, Xia D, Feng Z, Chen J, Li Z, Hu L, Meng Q

“…Using machine learning algorithms, Hub PCD-related differentially expressed genes (Hub PCD-DEGs) were identified. …”
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Continual Learning With Neuromorphic Computing: Foundations, Methods, and Emerging Applications by Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad, Osman Hasan, Muhammad Shafique

“…., sparse spike-driven operations and bio-plausible learning rules) for improving energy efficiency and performance, thereby enabling efficient CL algorithms (e.g., unsupervised learning approach) executed in dynamically-changed environments with resource-constrained computing systems. …”
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Characterizing Duodenal Immune Microenvironment in Functional Dyspepsia: An AutoML-Driven Diagnostic Framework by Zhang X, Fan X, Hu X, Qian Z, Li J, Wu W, Chen L, Wu S, Ma L, Yang C, Zhang T, Su X, Wei W

“…Immune infiltration was assessed using CIBERSORT and xCell algorithms, followed by weighted gene co-expression network analysis (WGCNA) to identify immune-related gene modules. …”
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Exploring the role of mitochondrial metabolism and immune infiltration in myocardial infarction: novel insights from bioinformatics and experimental validation by Jingyi Hou, Jingyi Hou, Jingyi Hou, Lihan Wang, Lihan Wang, Naiqiang Zhu, Xueli Li

“…A protein-protein interaction (PPI) network of mitoDEGs was constructed, and hub mitoDEGs associated with MI were identified using CytoHubba and molecular complex detection (MCODE) algorithms. Transcription factor (TF) and microRNA (miRNA) targets of hub mitoDEGs were predicted using iRegulon and miRWalk plug-ins, respectively, and a regulatory network involving TFs, hub mitoDEGs, and miRNA was established. …”
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Predictive value of the stone-free rate after percutaneous nephrolithotomy based on multiple machine learning models by Zhao Rong Liu, Zhao Rong Liu, Zhan Jiang Yu, Jie Zhou, Jian Biao Huang

“…In addition, the SHAP analysis identified the number of stones and the stone CT value as the most critical features influencing the model’s predictions, contributing significantly to its overall predictive performance.ConclusionThe prediction models developed based on three machine learning algorithms can accurately predict the stone-free rate after PCNL in patients with urinary tract stones. …”
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Combined Analysis of Transcriptome and Mendelian Randomization Reveals AKT1 and PPARG as Biomarkers Related to Glucose Metabolism in Sepsis by Ma J, Li W, Ma Q, Ding L, Wang Z, Wang R, Huang Y, Ma G, Gao J

“…Jie Ma,1,2,&amp;ast; Wendi Li,3 Qianqian Ma,1 Liying Ding,4 Zhaoyun Wang,1 Rong Wang,1 Yanan Huang,1 Gang Ma,2,&amp;ast; Jun Gao1,&amp;ast; 1Department of Anesthesia and Perioperative Medicine, First People’s Hospital of Yinchuan, The Second Clinical Medical College of Ningxia Medical University, Yinchuan, Ningxia, 750001, People’s Republic of China; 2Department of Anesthesia and Perioperative Medicine, General Hospital of Ningxia Medical University, The First Clinical Medical College of Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of China; 3Department of Child Rehabilitation Education, Ningxia Rehabilitation Center for the Disabled, Yinchuan, Ningxia, 750002, People’s Republic of China; 4Department of Gynecology, Gynecology Clinic of Li Ying, Wuzhong, Ningxia, 751100, People’s Republic of China&amp;ast;These authors contributed equally to this workCorrespondence: Gang Ma, Department of Anesthesia and Perioperative Medicine, General Hospital of Ningxia Medical University, The First Clinical Medical College of Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of China, Email magang2671@163.com Jun Gao, Department of Anesthesia and Perioperative Medicine, First People’s Hospital of Yinchuan, The Second Clinical Medical College of Ningxia Medical University, Yinchuan, Ningxia, 750001, People’s Republic of China, Email gaojun1605@sina.comIntroduction: This study aimed to identify diagnostic and therapeutic biomarkers related to glucose metabolism in sepsis, as hyperglycemia and blood glucose fluctuations influence sepsis progression.Methods: Datasets from public databases were analyzed using various methods, including differential expression analysis, PPI network screening, machine learning algorithms and Mendelian randomization. A nomogram model was developed, and biomarker functions were explored through enrichment analysis, immunoinfiltration analysis, transcription factors (TFs) and microRNA (miRNA) prediction, and drug prediction. …”
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Machine Learning–Based Prediction of Early Complications Following Surgery for Intestinal Obstruction: Multicenter Retrospective Study by Pinjie Huang, Jirong Yang, Dizhou Zhao, Taojia Ran, Yuheng Luo, Dong Yang, Xueqin Zheng, Shaoli Zhou, Chaojin Chen

“…ObjectiveThis study aimed to construct an online risk calculator for early postoperative complications in patients after intestinal obstruction surgery based on machine learning algorithms. MethodsA total of 396 patients undergoing intestinal obstruction surgery from April 2013 to April 2021 at an independent medical center were enrolled as the training cohort. …”
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A Convolutional Neural Network Tool for Early Diagnosis and Precision Surgery in Endometriosis-Associated Ovarian Cancer by Christian Macis, Miriam Santoro, Vladislav Zybin, Stella Di Costanzo, Camelia Alexandra Coada, Giulia Dondi, Pierandrea De Iaco, Anna Myriam Perrone, Lidia Strigari

“…Furthermore, the performance of each hybrid model and the majority voting ensemble of the three competing ML models were evaluated using trained and refined hybrid CNN models combined with Support Vector Machine (SVM) algorithms, with the best-performing model selected as the benchmark. …”
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