A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Subsequently, based on 482 prognostic moDEGs, we developed and validated an optimal model, termed the Monocyte-related Gene Prognostic Signature (MGPS), by integrating 101 predictive models generated from 10 machine learning algorithms across multiple transcriptome sequencing datasets. …”
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A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for lecanemab by Linlin Yan, Linhai Zhang, Zucai Xu, Zhong Luo

“…A biweekly 10 mg/kg was identified as the optimal therapeutic dosage. ARIA emerged as frequent treatment-related AEs, with APOEɛ4 carriers demonstrating heightened susceptibility. …”
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Unveiling PANoptosis in Acute Kidney Injury: An Integrative Multi-Dimensional Approach to Identify Key Biomarkers by Wang N, Zhang L, Xu Z, Xu Q, Lu Y, Niu P, Yan L, Wang L, Cao H, Shao F

“…Several machine learning algorithms were employed to determine the optimal feature genes. …”
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Comparison of Random Survival Forest Based‐Overall Survival With Deep Learning and Cox Proportional Hazard Models in HER‐2‐Positive HR‐Negative Breast Cancer by Wenqi Cai, Yan Qi, Linhui Zheng, Huachao Wu, Chunqian Yang, Runze Zhang, Chaoyan Wu, Haijun Yu

“…Predictive models were developed using five feature sets and three algorithms (Cox PH, RSF, DeepSurv), with feature selection optimized via Concordance index (C‐index). …”
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‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion by Manash Sarma, Subarna Chatterjee

“…Our research identified a subset of 95 gene transcripts exhibiting optimal efficacy from an extensive collection of over 49,000 transcripts within the ADNI gene expression dataset. …”
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Discovery of Potent Dengue Virus NS2B-NS3 Protease Inhibitors Among Glycyrrhizic Acid Conjugates with Amino Acids and Dipeptides Esters by Yu-Feng Lin, Hsueh-Chou Lai, Chen-Sheng Lin, Ping-Yi Hung, Ju-Ying Kan, Shih-Wen Chiu, Chih-Hao Lu, Svetlana F. Petrova, Lidia Baltina, Cheng-Wen Lin

“…We utilized docking algorithms to evaluate the interactions of these GL derivatives with key residues (His51, Asp75, Ser135, and Gly153) within 10 Å of the DENV-2 NS2B-NS3 protease binding pocket (PDB ID: 2FOM). …”
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Population-based colorectal cancer risk prediction using a SHAP-enhanced LightGBM model by Guinian Du, Hui Lv, Yishan Liang, Jingyue Zhang, Qiaoling Huang, Guiming Xie, Xian Wu, Hao Zeng, Lijuan Wu, Jianbo Ye, Wentan Xie, Xia Li, Yifan Sun

“…BackgroundColorectal cancer (CRC) is a highly frequent cancer worldwide, and early detection and risk stratification playing a critical role in reducing both incidence and mortality. we aimed to develop and validate a machine learning (ML) model using clinical data to improve CRC identification and prognostic evaluation.MethodsWe analyzed multicenter datasets comprising 676 CRC patients and 410 controls from Guigang City People’s Hospital (2020-2024) for model training/internal validation, with 463 patients from Laibin City People’s Hospital for external validation. Seven ML algorithms were systematically compared, with Light Gradient Boosting Machine (LightGBM) ultimately selected as the optimal framework. …”
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Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP) by Tim Rahmel, Michael Adamzik, Hartmuth Nowak, Lars Bergmann, Björn Koos, Martin Eisenacher, Barbara Sitek, Britta Marko, Lars Palmowski, Andrea Witowski, Katharina Rump, Julia Bandow, Patrick Günther

“…Our two-way approach involves creating two distinct algorithms: the first focuses on predictive accuracy and generalisability using routine clinical parameters, while the second leverages an extended dataset including a plethora of factors currently insufficiently explored and not available in standard clinical practice but may help to enhance precision. …”
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In-Silico discovery of novel cephalosporin antibiotic conformers via ligand-based pharmacophore modelling and de novo molecular design by Rayhan Chowdhury, Samia Akter Saima, Md. Al Amin, Md. Kawsar Habib, Ramisa Binti Mohiuddin, Ali Mohamod Wasaf Hasan, Roksana Khanam, Shahin Mahmud

“…This research aims to predict new compounds using ligand-based pharmacophore models while optimizing existing drugs. We employed a de novo approach to synthesize models of cephalosporin structural motifs, integrating the β-lactam core with potential antibiotic candidates. …”
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Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River by Mohammad Uzair Anwar Qureshi, Afshin Amiri, Isa Ebtehaj, Silvio José Guimere, Juraj Cunderlik, Hossein Bonakdari

“…The Next-Gen GMDH addresses the complexity and limitations of standard GMDH by incorporating non-adjacent connections and optimizing intermediate layers, significantly reducing computational overhead while enhancing performance. …”
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Screening of Reference Genes for Quantitative Real-time PCR in Curcuma alismatifolia Bracts by Jianjun TAN, Lishan HUANG, Yiwei ZHOU, Yechun XU, Yuanjun YE

“…The RefFinder program was utilized to comprehensively assess the optimal reference genes for C. alismatifolia bract.…”
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CNN–Transformer Hybrid Architecture for Underwater Sonar Image Segmentation by Juan Lei, Huigang Wang, Zelin Lei, Jiayuan Li, Shaowei Rong

“…FLSSNet is built upon a CNN and Transformer backbone network, integrating four core submodules to address various technical challenges: (1) The asymmetric dual encoder–decoder (ADED) is capable of simultaneously extracting features from different modalities and systematically modeling both local contextual information and global spatial structure. (2) The Transformer feature converter (TFC) module optimizes the multimodal feature fusion process through feature transformation and channel compression. (3) The long-range correlation attention (LRCA) module enhances CNN’s ability to model long-range dependencies through the collaborative use of convolutional kernels, selective sequential scanning, and attention mechanisms, while effectively suppressing noise interference. (4) The recursive contour refinement (RCR) model refines edge contour information through a layer-by-layer recursive mechanism, achieving greater precision in boundary details. …”
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Evolution, reconfiguration and low-carbon performance of green space pattern under diverse urban development scenarios: A machine learning-based simulation approach by Yujie Ren, Mengdie Zhou, Antian Zhu, Shucheng Shi, Hao Zhu, Yuzhu Chen, Shanshan Li, Tianhui Fan

“…In cities prioritizing ecological restoration, future carbon emissions tend to stabilize, whereas in rapidly growing megacities, despite recent declines in carbon emissions, further optimization of green space configurations is required to effectively manage emissions and bolster sequestration. …”
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The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review by Norah Hamad Alhumaidi, Doni Dermawan, Hanin Farhana Kamaruzaman, Nasser Alotaiq

“…The search focused on extracting data regarding the ML algorithms applied; disease categories studied; types of study designs (eg, clinical trials and cohort studies); and the sources of RWE, including EHRs, patient registries, and wearable devices. …”
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Benchmarking Federated Few-Shot Learning for Video-Based Action Recognition by Nguyen Anh Tu, Nartay Aikyn, Nursultan Makhanov, Assanali Abu, Kok-Seng Wong, Min-Ho Lee

“…Additionally, we explore three meta-learning paradigms and three FL algorithms to investigate their effectiveness and suggest the optimal choices for performance improvement. …”
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Human Action Recognition Method Based on Multi-channel Fusion by Zhiyong TAO, Xijun GUO, Xiaokui REN, Ying LIU, Zemin WANG

“…This operation preserves the action characteristics of each channel while facilitating a global comparison of these characteristics. …”
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Artificial intelligence-based prediction of second stage duration in labor: a multicenter retrospective cohort analysisResearch in context by Xiaoqing Huang, Xiaodan Di, Suiwen Lin, Minrong Yao, Suijin Zheng, Shuyi Liu, Wayan Lau, Zhixin Ye, Zilian Wang, Bin Liu

“…Since durations beyond 3 h were rare, we developed binary classification models with thresholds at 1 h and 2 h. After the optimal features selected by recursive feature elimination (RFE) method, four ML algorithms were employed to build the models. …”
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A Comparison of Recent Global Time-Series Land Cover Products by Peilin Li, Yan Wang, Chisheng Wang, Lin Tian, Meijiao Lin, Siyao Xu, Chuanhua Zhu

“…High-resolution products (e.g., GLC_FCS30D, Dynamic World) are optimal for monitoring fragmented landscapes and urban expansion, whereas long-term datasets (e.g., ESA CCI, MCD12Q1) suit climate trend analysis in stable ecosystems. …”
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Multi-omics characterization of diabetic nephropathy in the db/db mouse model of type 2 diabetes by Liping Wang, Ran Zhou, Guanghui Li, Xiaodan Zhang, Yan Li, Yinchen Shen, Junwei Fang

“…Background: Despite optimized blood pressure control and glycemic management reducing the incidence of diabetic nephropathy (DN), significant residual risk remains, suggesting the contribution of pathogenic factors independent of glucose metabolism and hemodynamic disturbances. …”
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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

“…Background: With the introduction of sensing-enabled deep brain stimulation devices, characterization of long-term biomarker dynamics is of growing importance for treatment optimization. The microlesion effect is a well-known phenomenon of transient clinical improvement in the acute post-operative phase. …”
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