Spatiotemporal Urban Evolution Along the China–Laos Railway in Laos Determined Using Multiple Sources of Remote-Sensed Landscape Indicators and Interpretable Machine Learning by Dongxue Li, Jin Tang, Qiao Hu, Mingjuan Dong, Soukanh Chithpanya

“…Data-driven machine learning and causal inference approaches are integrated to quantify the spatial–temporal evolution and discover its driving factors. …”
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A Construction Method for a Coal Mining Equipment Maintenance Large Language Model Based on Multi-Dimensional Prompt Learning and Improved LoRA by Xiangang Cao, Xulong Wang, Luyang Shi, Xin Yang, Xinyuan Zhang, Yong Duan

“…Additionally, a fine-tuning approach based on the joint optimization of a mixture of experts (MoE) and low-rank adaptation (LoRA) is introduced, which employs multiple expert networks and task-driven gating functions to achieve the precise modeling of different maintenance tasks. …”
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Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables by Sergio Cofre-Martel, Enrique Lopez Droguett, Mohammad Modarres

“…Although the state-of-art results have been achieved in benchmark problems, most DL-PHM algorithms are treated as black-box functions, giving little-to-no control over data interpretation. …”
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Integrative analysis of lactylation related genes in prostate cancer: unveiling heterogeneity through single-cell RNA-seq, bulk RNA-seq and machine learning by Chenghao Zhou, Lifeng Ding, Huailan Wang, Gonghui Li, Lei Gao

“…The tumor microenvironment and functional enrichment of relevant pathways were also evaluated. …”
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Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning by Weiliang Zhang, Weirong Lu, Yaqi Jiao, Tianhao Li, Haining Wang, Chunhua Wan

“…Additionally, Protein-Protein Interaction (PPI) networks, functional enrichment, and immune infiltration analyses were performed. …”
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The Stockholm Spinal Cord Uro Study: Changing patterns of urological surgery in a regional prevalence group through 50 years – Outcomes and lessons learned by Elisabeth Farrelly, Lena Lindbo, Åke Seiger

“…After reconstructive surgery functional outcomes and patient-reported satisfaction were generally favourable, but long-term signs of renal complications were frequent. …”
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RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients by Xiliang Zhang, Hanjie Guo, Xiaolong Li, Wei Tao, Xiaoqing Ma, Yuxing Zhang, Weidong Xiao

“…These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. …”
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Quantifying Ecological Dynamics and Anthropogenic Dominance in Drylands: A Hybrid Modeling Framework Integrating MRSEI and SHAP-Based Explainable Machine Learning in Northwest Chin... by Beilei Zhang, Xin Yang, Mingqun Wang, Liangkai Cheng, Lina Hao

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Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine learning by Changjin Wei, Yongfeng Zhu, Caiming Chen, Feipeng Li, Li Zheng

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SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI by Ami Tsuchida, Martin Goubet, Philippe Boutinaud, Iana Astafeva, Victor Nozais, Pierre-Yves Hervé, Thomas Tourdias, Stéphanie Debette, Marc Joliot

“…We believe this effort will help accelerate research on the pathophysiology and functional consequences of CMB by enabling rapid characterization of CMB in large-scale studies.…”
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Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approach... by Tathagat Banerjee, Prachi Chhabra, Manoj Kumar, Abhay Kumar, Kumar Abhishek, Mohd. Asif Shah

“…Abstract Brain tumors are a significant challenge to human health as they impair the proper functioning of the brain and the general quality of life, thus requiring clinical intervention through early and accurate diagnosis. …”
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Integrative bioinformatics and machine learning identify iron metabolism genes MAP4, GPT, and HIRIP3 as diagnostic biomarkers and therapeutic targets in Alzheimer’s disease by Xiaoqiong An, Xiangguang Zeng, Zhenzhen Yi, Manni Cao, Yijia Wang, Wenfeng Yu, Wenfeng Yu, Zhenkui Ren, Zhenkui Ren

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Integrated machine learning and single-cell RNA sequencing reveal COL4A2 and CXCL6 as oxidative stress-associated biomarkers in periodontitis by Siyu Sun, Siyu Sun, Jing Ren, Jing Ren, Xiujuan Zeng, Yanbin Chen, Qianbing Zhou, Junying Yang, Shan Chen

“…Key genes linked to oxidative stress in periodontitis were identified through a comprehensive analytical approach, including differential expression analysis, weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), and functional enrichment analyses (GO and KEGG). Machine learning algorithms were subsequently employed to refine the selection of key genes. …”
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Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning by Zuhui Pu, Zuhui Pu, Tony Bowei Wang, Ying Lu, Ying Lu, Zijing Wu, Zijing Wu, Yuxian Chen, Ziqi Luo, Xinyu Wang, Lisha Mou, Lisha Mou

“…We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). …”
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Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Effic... by Shadi Jacob Khoury, Yazeed Zoabi, Mickey Scheinowitz, Noam Shomron

“…By applying interpretability techniques to both machine learning and deep learning models, we aimed to improve understanding of symptomatology and enhance early detection of COVID-19 cases. …”
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Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrou... by Tianyu Yang, Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao

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Integrating machine learning and multi-omics analysis to unveil key programmed cell death patterns and immunotherapy targets in kidney renal clear cell carcinoma by Fanyan Ou, Yi Pan, Qiuli Chen, Lixiong Zeng, Kanglai Wei, Delin Liu, Qian Guo, Liquan Zhou, Jie Yang

“…Integrating multi-omics analysis, including immune cell infiltration, phenotyping, functional analysis, immune checkpoint exploration, and gene set enrichment analysis (GSEA), we explored the relationship between key cell death patterns and patients’ responses to immunotherapy. …”
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Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China by Weizi Wu, Li Jing, Qing Peng, Peng Hua, Zeng Shumei, Luofang Lv, Liqing Yue, Hu Jian zhong, Huang Weihong

“…Hyperparameter optimisation was performed using stratified fivefold cross-validation with grid search, incorporating class-weighted loss functions to address class imbalance and model-specific regularisation techniques to maximise performance while preventing overfitting. …”
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Feasibility study of Learning Together for Mental Health: fidelity, reach and acceptability of a whole-school intervention aiming to promote health and wellbeing in secondary schoo... by Neisha Sundaram, Oliver Lloyd-Houldey, Joanna Sturgess, Elizabeth Allen, Semina Michalopoulou, Steven Hope, Rosa Legood, Stephen Scott, Lee D Hudson, Dasha Nicholls, Deborah Christie, Russell M Viner, Chris Bonell

“…This was effective not only in preventing bullying (primary outcome) but also in promoting mental well-being and psychological functioning (secondary outcomes). We adapted Learning Together to develop Learning Together for Mental Health, focused on promoting mental health. …”
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Do Intellectually Gifted Children Have Better Planning Skills? by Li Cheng, Xiaohe Xie, Shiting Yang, Linjie Xiao, Xiaoyu Chen, Yun Nan, Dong Qi, Jagannath P. Das, George K. Georgiou

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