Random Undersampled Digital Elevation Model Super-Resolution Based on Terrain Feature-Aware Deep Learning Network by Ziqiang Huo, Meng Xi, Jingyi He, Zhengjian Li, Jiabao Wen

“…Meanwhile, the general spatial interpolation algorithms based on deep learning usually have low model complexity and lack the specific designed loss function, which usually leads to significant interpolation errors. …”
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Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence by Manohar Pavanya, Krishnaraj Chadaga, Vennila J, Akhila Vasudeva, Bhamini Krishna Rao, Srikanth Prabhu, Shashikala K Bhat

“…We developed a stacked ensemble model that integrated various algorithms, including a custom-stacked ensemble approach and three XAI methodologies: Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Anchor. …”
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Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings by Xiao-Han Vivian Yap, Kuan-Chi Tu, Nai-Ching Chen, Che-Chuan Wang, Chia-Jung Chen, Chung-Feng Liu, Tee-Tau Eric Nya, Ching-Lung Kuo

“…Abstract Background Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims to construct a mortality prediction model for patients with SICH using four various artificial intelligence (AI) machine learning algorithms. …”
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Exploring T-cell metabolism in tuberculosis: development of a diagnostic model using metabolic genes by Shoupeng Ding, Chunxiao Huang, Jinghua Gao, Chun Bi, Yuyang Zhou, Zihan Cai

“…We identified T-cell-associated metabolic differentially expressed genes (TCM–DEGs) through integrated differential expression analysis and machine learning algorithms (XGBoost, SVM–RFE, and Boruta). …”
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Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis by Dongxu Qin, Yongquan Zheng, Libo Wang, Zhenyi Lin, Yao Yao, Weidong Fei, Caihong Zheng

“…ROC analysis showed that the Area Under the Curve (AUC) values for individual genes in disease diagnosis were all above 0.7. The constructed clinical prediction model demonstrated robust predictive capacity for the disease. …”
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Ensemble Learning for Spatial Modeling of Icing Fields from Multi-Source Remote Sensing Data by Shaohui Zhou, Zhiqiu Gao, Bo Gong, Hourong Zhang, Haipeng Zhang, Jinqiang He, Xingya Xi

“…In this study, we propose a new approach for constructing real-time icing grid fields using 1339 online terminal monitoring datasets provided by the China Southern Power Grid Research Institute Co., Ltd. …”
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Proteomic signatures and predictive modeling of cadmium-associated anxiety in middle-aged and elderly populations: an environmental exposure association study by Sheng Wan, Yong Yang, Qihan Zhao, Zelong Xing, Jie Li, Hao Gao, Yinghui Yin, Zhenzhong Liu, Qiwen Chen, Maoqin Tian, Xinxin Shi, Ziyue Ji, Shaoxin Huang

“…Finally, clinical variables were integrated to construct a comprehensive model, which was then thoroughly evaluated. …”
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Radiomic Analysis of Contrast‐Enhanced CT Predicts Recompensation in Hepatitis B‐Related Decompensated Cirrhosis by Qiaofeng Chen, Yuanping Fan, Kaini Wu, Tianpan Cai, Chunyu Lan, Mingju Yu, Yunfeng Fu, Qi Zhu, Jianhao Qiu, Xiaodong Zhou

“…Independent clinical factors were identified using logistic regression. A combined model integrating radiomic signatures and clinical factors was then constructed. …”
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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

“…These candidates were then fused with the cephalosporin core using genetic algorithms and fragment-based design, resulting in 30 novel synthetic models. …”
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Enhancing maize LAI estimation accuracy using unmanned aerial vehicle remote sensing and deep learning techniques by Zhen Chen, Weiguang Zhai, Qian Cheng

“…Subsequently, spectral features, texture features, and crop height were extracted from the multi-spectral remote sensing data to construct a multi-source feature dataset. Then, maize LAI estimation models were developed using multiple linear regression, gradient boosting decision tree, and CNN. …”
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Smart CAR-T Nanosymbionts: archetypes and proto-models by Juan C. Baena, Juan C. Baena, Juan Sebastián Victoria, Juan Sebastián Victoria, Alejandro Toro-Pedroza, Alejandro Toro-Pedroza, Cristian C. Aragón, Cristian C. Aragón, Joshua Ortiz-Guzman, Joshua Ortiz-Guzman, Joshua Ortiz-Guzman, Juan Esteban Garcia-Robledo, Juan Esteban Garcia-Robledo, David Torres, Lady J. Rios-Serna, Lady J. Rios-Serna, Ludwig Albornoz, Joaquin D. Rosales, Joaquin D. Rosales, Carlos A. Cañas, Carlos A. Cañas, Carlos A. Cañas, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Felipe Ocampo Osorio, Felipe Ocampo Osorio, Felipe Ocampo Osorio, Tania Fleitas, Ivan Laponogov, Alexandre Loukanov, Alexandre Loukanov, Alexandre Loukanov, Kirill Veselkov, Kirill Veselkov

“…In the context of CAR-T, AI holds strong potential for better patient stratification, improved prediction of treatment response and toxicity, and faster, more precise design of CAR constructs and delivery systems. Leveraging these three technological pillars, this review introduces the concept of Smart CART Nanosymbionts, an integrated framework in which AI guides the design and deployment of nanotechnology-enhanced CAR-T therapies. …”
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Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort by Tahzeeb Fatima, Yuan Zhang, Georgios K. Vasileiadis, Araz Rawshani, Ronald van Vollenhoven, Jon Lampa, Bjorn Gudbjornsson, Espen A. Haavardsholm, Dan Nordström, Gerdur Gröndal, Kim Hørslev-Petersen, Kristina Lend, Marte S. Heiberg, Merete Lund Hetland, Michael Nurmohamed, Mikkel Østergaard, Till Uhlig, Tuulikki Sokka-Isler, Anna Rudin, Cristina Maglio

“…Machine learning models for treatment response were constructed using random forest, logistic regression, support vector machine and extreme gradient boosting algorithms based on selected features. …”
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ATP6V0A4 as a novel prognostic biomarker and potential therapeutic target in oral squamous cell carcinoma by Xiaopu Gao, Jiamin Zhou, Yu Qiao, Chuyin Lin, Guanxiong Zhang, Qiuyu Wu, Zhikang Su, Qianji Zhang, Songkai Huang

“…A prognostic model was constructed using univariate Cox regression and LASSO regression, complemented by random forest algorithms to identify core genes. …”
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Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury by Chen Lin, Meng Zheng, Wensi Wu, Zhishan Wang, Guofeng Lu, Shaodan Feng, Xinlan Zhang

“…We assessed 113 combinations of 12 different algorithms to develop an internally and externally validated machine-learning model for diagnosing AKI. …”
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A gene signature related to programmed cell death to predict immunotherapy response and prognosis in colon adenocarcinoma by Lei Zheng, Jia Lu, Dalu Kong, Yang Zhan

“…Immune infiltration of the samples was evaluated using CIBERSORT and Microenvironment Cell Populations (MCP)-counter algorithms. Patients’ immunotherapy response was predicted by the TIDE and aneuploidy scores. …”
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Habitat radiomics analysis for progression free survival and immune-related adverse reaction prediction in non-small cell lung cancer treated by immunotherapy by Yuemin Wu, Wei Zhang, Xiao Liang, Pengpeng Zhang, Mengzhe Zhang, Yuqin Jiang, Yanan Cui, Yi Chen, Wenxin Zhou, Qi Liang, Jiali Dai, Chen Zhang, Jiali Xu, Jun Li, Tongfu Yu, Zhihong Zhang, Renhua Guo

“…Results Our study introduces a radiomic nomogram model that integrates clinical and habitat radiomic features to identify patients who may benefit from ICIs or experience irAEs. …”
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Automated machine learning for predicting perioperative ischemia stroke in endovascularly treated ruptured intracranial aneurysm patients by Yuhang Peng, Ke Bi, Xiaolin Zhang, Ning Huang, Xiang Ji, Weifu Chen, Ying Ma, Yuan Cheng, Yongxiang Jiang, Jianhe Yue

“…Nine machine learning algorithms were evaluated using stratified 10-fold cross-validation. …”
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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

“…ConclusionsThis study evaluates prediction models constructed using various ML algorithms, with results showing that ML demonstrates high performance in ARDS prediction. …”
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A novel, rapid, and practical prognostic model for sepsis patients based on dysregulated immune cell lactylation by Chang Li, Mei He, PeiChi Shi, Lu Yao, XiangZhi Fang, XueFeng Li, QiLan Li, XiaoBo Yang, JiQian Xu, You Shang, You Shang

“…Patients were stratified into subgroups using k-means clustering based on lactylation levels. Machine learning algorithms, integrated with pseudotime trajectory reconstruction, were employed to map the temporal dynamics of lactylation. …”
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Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis by Peize Yu, Peize Yu, Qikai Zhong, Qikai Zhong, Xinlei Wang, Xinlei Wang, Yifang Liu, Qiang Liu, Qiang Liu, Yuqiang Zhang, Jiawei Lu, Yang Dong, Cong-hui Han, Cong-hui Han

“…Molecular docking and pan-cancer analyses were conducted to identify therapeutic candidates and ATP1A1-related mechanisms.ResultsFive AICD-related genes (FOXM1, ANK3, ATP1A1, HADH, and PLG) were identified and selected to construct a risk score model. The model demonstrated high accuracy and was integrated into a nomogram for clinical application. …”
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