Preoperative ternary classification using DCE-MRI radiomics and machine learning for HCC, ICC, and HIPT by Peng Xie, Zhong-Jian Liao, Lu Xie, Junyuan Zhong, Xiaodong Zhang, Wei Yuan, Yujin Yin, Tianxian Chen, Huizhen Lv, Xinglin Wen, Xiaochun Wang, Ling Zhang

“…Rim enhancement is a key model feature for distinguishing HCC from ICC and HIPT, while hepatic lobe atrophy distinguishes ICC and HIPT from HCC. …”
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Generation of Spatial Structure of Urban Parks Based on Spatial Analysis of Agent-Based Models by Huizi KONG, Tianming LIU, Jiajie LIAO, Liu CUI

“…ObjectiveThis research aims to leverage the advances of computer algorithms to provide scientific analytical methods for spatial simulation and offer a more objective approach to reflect the natural operational laws of interdependencies between elements in the real physical world for landscape design. …”
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Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification by Andrea González-Ramírez, Clement Atzberger, Deni Torres-Roman, Josué López

“…To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. …”
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A comparative study of bone density in elderly people measured with AI and QCT by Min Guo, Min Guo, Yu Zhang, Yu Zhang, XinXin Gu, XinXin Gu, Xuhui Liu, Xuhui Liu, Fei Peng, Fei Peng, Zongjun Zhang, Zongjun Zhang, Mei Jing, Mei Jing, Yingxia Fu, Yingxia Fu

“…Our data suggest that AI-driven BMD quantification demonstrates non-inferior diagnostic accuracy to QCT while overcoming DXA’s accessibility limitations. …”
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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

“…ABSTRACT Background Traditional CoxPH models are limited in handling real‐world data complexities. While machine learning models like RSF and DeepSurv show promise, their application and comparative evaluation in the HER2‐positive/HR‐negative breast cancer subtype require further validation. …”
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LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars by Ya Fei, Ce Yu, Kun Li, Xiaodian Chen, Yajie Zhang, Chenzhou Cui, Jian Xiao, Yunfei Xu, Yihan Tao

“…Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy, which means it can be directly applied to different data types with only a relatively small loss in performance compared to a dedicated model.…”
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Immune training enhances anti-viral responses and improves outcomes in Pax5 −/+ mice susceptible to chronic infection by Zhe Lu, Olivia Stencel, Wei Liu, Eleni Vasileiou, Haifeng C Xu, Piyush Pandey, Paweł Stachura, Abdelrahman Elwy, Anastassia Tsombal, Ann-Sophie Mai, Franziska Auer, Mina N F Morcos, Maximilian Seidl, Sarah Koziel, Peter-Martin Bruch, Sascha Dietrich, Sarah Elitzur, Gunther Hartmann, Karl S Lang, Stefan Janssen, Ute Fischer, Sanil Bhatia, Philipp A Lang, Arndt Borkhardt, Julia Hauer, Aleksandra A Pandyra

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Satellite Image Price Prediction Based on Machine Learning by Linhan Yang, Zugang Chen, Guoqing Li

“…This study develops a comprehensive, data-driven framework for predicting satellite imagery prices using four state-of-the-art ensemble learning algorithms: XGBoost, LightGBM, AdaBoost, and CatBoost. …”
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A Framework for High-Spatiotemporal-Resolution Soil Moisture Retrieval in China Using Multi-Source Remote Sensing Data by Zhuangzhuang Feng, Xingming Zheng, Xiaofeng Li, Chunmei Wang, Jinfeng Song, Lei Li, Tianhao Guo, Jia Zheng

“…Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). …”
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Demethylase FTO mediates m6A modification of ENST00000619282 to promote apoptosis escape in rheumatoid arthritis and the intervention effect of Xinfeng Capsule by Fanfan Wang, Fanfan Wang, Jianting Wen, Jianting Wen, Jian Liu, Jian Liu, Ling Xin, Yanyan Fang, Yanyan Fang, Yue Sun, Mingyu He, Mingyu He

“…After XFC treatment, FTO, ENST00000619282, and Bcl-2 expressions were decreased, while YTHDF1 and Bax expressions were increased (all P<0.05). …”
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Comparison of cardiovascular risk prediction models developed using machine learning based on data from a Sri Lankan cohort with World Health Organization risk charts for predictin... by Anuradhani Kasturiratne, Hithanadura Janaka de Silva, Chamila Mettananda, Anuradha Supun Dassanayake, Norihiro Kato, Rajitha Wickremasinghe, Maheeka Solangaarachchige, Prasanna Haddela

“…WHO risk charts predicted only 10 CVEs (AUC-ROC: 0.51, 95% CI 0.42 to 0.60), while the new 6-variable ML model predicted 125 CVEs (AUC-ROC: 0.72, 95% CI 0.66 to 0.78) and the 75-variable ML model predicted 124 CVEs (AUC-ROC: 0.74, 95% CI 0.68 to 0.80). …”
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Translational medicine research on the role of key gene network modulation mediated by procyanidin B2 in the precise diagnosis and treatment of multiple sclerosis by Jian Liu, Meng Pu, Di Guo, Ying Xiao, Jin-zhu Yin, Dong Ma, Cun-gen Ma, Qing Wang

“…Eight machine learning algorithms were employed to screen key genes, and nomograms and ROC curves were constructed to assess the value of the screened biomarker genes in MS diagnosis. …”
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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

“…The predictive model offers translatable potential for early risk stratification, while CCDC126 provides mechanistic insights for targeted interventions in populations exposed to environmental pollutants. …”
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Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data by Theodoros Petropoulos, Lefteris Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis, Dionysis Bochtis

“…Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. …”
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Improving health-promoting workplaces through interdisciplinary approaches. The example of WISEWORK-C, a cluster of five work and health projects within Horizon-Europe by Deborah De Moortel, Michelle C Turner, Ella Arensman, Alex Binh Vinh Duc Nguyen, Víctor Gonzalez

“…These shifts are giving rise to new forms of work (eg, hybrid work, gig economy jobs) and reshaping management and work organization practices (eg, through algorithmic decision-making or digital monitoring of worker performance). …”
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An overview of the high-resolution global LAnd surface satellite (Hi-GLASS) products suite by Shunlin Liang, Tao He, Jie Cheng, Bo Jiang, Huaan Jin, Ainong Li, Siwei Li, Liangyun Liu, Xiaobang Liu, Han Ma, Dan-Xia Song, Lin Sun, Yunjun Yao, Wenping Yuan, Yufang Zhang, Feng Tian, Leshi Li

“…Although most of the algorithms have already been published in the literature, we have addressed the challenges of operational implementation to produce the suite of products. …”
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Study protocol: diagnosis of atrial fibrillation in postoperative thoracic surgery using a smartwatch, an open-label randomised controlled study (THOFAWATCH trial) by Osama Abou-Arab, Pierre Huette, Guillaume Haye, Mathieu Guilbart, Momar Diouf, Christophe Beyls, Herve Dupont, Yazine Mahjoub, Thomas Lefebvre, Azrat Ibrahima, Guillaume Bayart, Franck Lhotellier, Michael Radji, Katy-Anne Walczak, Matthieu Caboche, Florence De Dominicis, Olivier Georges, Pascal Berna, Geonie Merlusca, Alexis Hermida, Sarah Traullé, Michaël Bernasinski, Adrien Coupez, Patricia Besserve, Alice Henry, Camille Daumin, Ammar BenAmmar, Florent Leviel, Pierre-Yves Macq, Beatris Alina Labont, Gilles David, Julien Epailly, Paul-Emmanuel Esmard, Agathe Vernier, Pauline Bartoli, Jerôme Delmas, Sophie Romby, Simon Marx, Mohammed Fikri, Hélène Benoit-Fallet, Eric Nam, Jérome Zakine, Amandine Zaatar, Thomas Flet

“…Smartwatches equipped with single-lead ECG and atrial fibrillation (AF) detection algorithms offer a novel approach for early POAF detection. …”
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Circadian clock disruption impairs immune oscillation in chronic endogenous hypercortisolism: a multi-level analysis from a multicentre clinical trialResearch in context by Valeria Hasenmajer, Emilia Sbardella, Francesca Sciarra, Chiara Simeoli, Claudia Pivonello, Filippo Ceccato, Riccardo Pofi, Marianna Minnetti, Flavio Rizzo, Davide Ferrari, Ilaria Bonaventura, Federica Barbagallo, Elisa Giannetta, Danilo Alunni Fegatelli, Simone Conia, Roberto Navigli, Giorgio Arnaldi, Carla Scaroni, Rosario Pivonello, Daniele Gianfrilli, Mary Anna Venneri, Andrea M. Isidori

“…Cosinor mixed effects model, rhythmicity algorithms and machine learning techniques were applied to the multi-level dataset. …”
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Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adeno... by Song Lin, Song Lin, Yanli Niu, Yanli Niu, Lina Song, Yingjian Ye, Jinfang Yang, Junjie Liu, Xin Zhou, Xin Zhou, Peng An, Peng An

“…Ten machine learning algorithms (e.g., XGBoost, CatBoost, Random Forest) were trained on a stratified 7:3 split (training: n=245; testing: n=105) with five-fold cross-validation. …”
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Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…Recent research investigations have shown that Machine Learning (ML) algorithms can identify geochemical anomalies associated with mineralization that represent targets for mineral exploration. …”
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