A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…Based on this framework, we developed Model (i), which integrates SOC data with spatial-spectral resolution downscaling (SSD) image; Model (ii), which integrates SOC data, UAV image with spatial resolution downscaling (SD) image; and Model (iii), which integrates SOC data, UAV image with SSD image. We also evaluated the performance of various algorithms (e.g., Random Forest (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Multi-Layer Perceptron (MLP)) across these models. …”
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Multiple loci are associated with white blood cell phenotypes. by Michael A Nalls, David J Couper, Toshiko Tanaka, Frank J A van Rooij, Ming-Huei Chen, Albert V Smith, Daniela Toniolo, Neil A Zakai, Qiong Yang, Andreas Greinacher, Andrew R Wood, Melissa Garcia, Paolo Gasparini, Yongmei Liu, Thomas Lumley, Aaron R Folsom, Alex P Reiner, Christian Gieger, Vasiliki Lagou, Janine F Felix, Henry Völzke, Natalia A Gouskova, Alessandro Biffi, Angela Döring, Uwe Völker, Sean Chong, Kerri L Wiggins, Augusto Rendon, Abbas Dehghan, Matt Moore, Kent Taylor, James G Wilson, Guillaume Lettre, Albert Hofman, Joshua C Bis, Nicola Pirastu, Caroline S Fox, Christa Meisinger, Jennifer Sambrook, Sampath Arepalli, Matthias Nauck, Holger Prokisch, Jonathan Stephens, Nicole L Glazer, L Adrienne Cupples, Yukinori Okada, Atsushi Takahashi, Yoichiro Kamatani, Koichi Matsuda, Tatsuhiko Tsunoda, Toshihiro Tanaka, Michiaki Kubo, Yusuke Nakamura, Kazuhiko Yamamoto, Naoyuki Kamatani, Michael Stumvoll, Anke Tönjes, Inga Prokopenko, Thomas Illig, Kushang V Patel, Stephen F Garner, Brigitte Kuhnel, Massimo Mangino, Ben A Oostra, Swee Lay Thein, Josef Coresh, H-Erich Wichmann, Stephan Menzel, JingPing Lin, Giorgio Pistis, André G Uitterlinden, Tim D Spector, Alexander Teumer, Gudny Eiriksdottir, Vilmundur Gudnason, Stefania Bandinelli, Timothy M Frayling, Aravinda Chakravarti, Cornelia M van Duijn, David Melzer, Willem H Ouwehand, Daniel Levy, Eric Boerwinkle, Andrew B Singleton, Dena G Hernandez, Dan L Longo, Nicole Soranzo, Jacqueline C M Witteman, Bruce M Psaty, Luigi Ferrucci, Tamara B Harris, Christopher J O'Donnell, Santhi K Ganesh

“…We implemented gene-clustering algorithms to evaluate functional connectivity among implicated loci and showed functional relationships across cell types. …”
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Harnessing multi-omics and artificial intelligence: revolutionizing prognosis and treatment in hepatocellular carcinoma by Zhen Wang, Zhen Wang, Zhen Wang, Gangchen Zhou, Gangchen Zhou, Rongchuan Cao, Rongchuan Cao, Guolin Zhang, Guolin Zhang, Yongxu Zhang, Yongxu Zhang, Mingyue Xiao, Longbi Liu, Longbi Liu, Xuesong Zhang

“…To identify distinct molecular subtypes, a multi-omics data integration approach was employed, utilizing 10 distinct clustering algorithms. Survival analysis, immune infiltration profiling and drug sensitivity predictions were then used to evaluate the prognostic significance and therapeutic responses of these subtypes. …”
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Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy by Wuyuan Liao, Wuyuan Liao, Wuyuan Liao, Xinwei Zhou, Xinwei Zhou, Hansen Lin, Hansen Lin, Zihao Feng, Zihao Feng, Xinyan Chen, Yuhang Chen, Yuhang Chen, Minyu Chen, Minyu Chen, Mingjie Lin, Mingjie Lin, Gaosheng Yao, Gaosheng Yao, Jinwei Chen, Jinwei Chen, Haoqian Feng, Haoqian Feng, Yinghan Wang, Yinghan Wang, Zhiping Tan, Zhiping Tan, Youyan Tan, Jun Lu, Jun Lu, Pengju Li, Pengju Li, Jinhuan Wei, Jinhuan Wei, Li Luo, Li Luo, Liangmin Fu, Liangmin Fu, Liangmin Fu

“…We investigated its expression in tumor tissues and evaluated the impact of its knockdown on immunotherapeutic efficacy using in vitro and in vivo experiments.ResultsOur comprehensive analysis revealed that the predictive power of TMB varies significantly across different cancer types and is highly dependent on its interaction with the TME. …”
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Construction and analysis of a prognostic risk scoring model for gastric cancer anoikis-related genes based on LASSO regression by Ai CHEN, Xiaowei CHEN, Yanan WANG, Xiaobing SHEN

“…Gene expression levels in gastric cancer clinical samples and cells were detected by real-time quantitative PCR (RT-qPCR); Kaplan-Meier (KM) survival curves, univariate and multivariate Cox regression analyses were used to verify the predictive efficiency of the prognostic risk scoring model for the prognosis of gastric cancer patients; CIBERSORT and ESTIMATE algorithms were used to analyze the immune cell infiltration levels in patients with different risk groups; the correlation between risk scores and immune checkpoint expression levels in gastric cancer patients was analyzed using the R package "ggplot2" and "ggExtra", and the correlation between tumor mutation burden (TMB) and risk scores was assessed; chemotherapy drug sensitivity analysis was used to evaluate the value of the constructed prognostic risk scoring model in gastric cancer chemotherapy. …”
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Editorial by Bulent Cavas

“…The tenth article, “Hybrid Teacher Training in Arduino-Based Science Education Across Different Modalities,” by Sarah et al. (Indonesia and Australia), evaluates a training program that equips teachers with Arduino-based science project skills. …”
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What happens between first symptoms and first acute exacerbation of COPD – observational study of routine data and patient survey by Alex Bottle, Alex Adamson, Xiubin Zhang, Benedict Hayhoe, Jennifer K Quint

“…However, there is limited understanding of what prompts a diagnosis, how long this takes from symptom onset and the different approaches to clinical management by primary care professionals. …”
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Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer by Lin Ni, Lin Ni, He Li, Yanqi Cui, Wanqiu Xiong, Shuming Chen, Hancong Huang, Zhiwei Wang, Hu Zhao, Hu Zhao, Hu Zhao, Bing Wang, Bing Wang, Bing Wang

“…ObjectivesIn this study, we constructed a model based on circadian rhythm associated genes (CRRGs) to predict prognosis and immune infiltration in patients with breast cancer (BC).Materials and methodsBy using TCGA and CGDB databases, we conducted a comprehensive analysis of circadian rhythm gene expression and clinicopathological data. Three different machine learning algorithms were used to screen out the characteristic circadian genes associated with BC prognosis. …”
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Clinical Validation of a Machine Learning-Based Biomarker Signature to Predict Response to Cytotoxic Chemotherapy Alone or Combined with Targeted Therapy in Metastatic Colorectal C... by Duilio Pagano, Vincenza Barresi, Alessandro Tropea, Antonio Galvano, Viviana Bazan, Adele Caldarella, Cristina Sani, Gianpaolo Pompeo, Valentina Russo, Rosa Liotta, Chiara Scuderi, Simona Mercorillo, Floriana Barbera, Noemi Di Lorenzo, Agita Jukna, Valentina Carradori, Monica Rizzo, Salvatore Gruttadauria, Marco Peluso

“…Current treatments are limited and not always effective because the cancer responds differently to drugs in different patients. This research aims to use artificial intelligence (AI) to improve treatment by predicting which therapies will work best for individual patients. …”
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Diagnosis and activity prediction of SLE based on serum Raman spectroscopy combined with a two-branch Bayesian network by Qianxi Xu, Qianxi Xu, Qianxi Xu, Xue Wu, Xue Wu, Xue Wu, Xinya Chen, Ziyang Zhang, Jinrun Wang, Jinrun Wang, Zhengfang Li, Zhengfang Li, Zhengfang Li, Xiaomei Chen, Xiaomei Chen, Xiaomei Chen, Xin Lei, Xin Lei, Zhuoyu Li, Zhuoyu Li, Zhuoyu Li, Mengsi Ma, Mengsi Ma, Mengsi Ma, Chen Chen, Lijun Wu, Lijun Wu

“…A comparison was made between the proposed DBayesNet classification model and traditional machine and deep learning algorithms, including KNN, SVM, RF, LDA, ANN, AlexNet, ResNet, LSTM, and ResNet. …”
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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

“…Subsequently, six ML models were evaluated representing different algorithmic strategies. …”
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Establishment of a prognostic model based on ER stress-related cell death genes and proposing a novel combination therapy in acute myeloid leukemia by Minghui Wang, Huajian Xian, Xiaoli Xia, Wenjie Zhang, Zixuan Huang, Chaoqun Lu, Yuling Zheng, Yixin Wang, Shufeng Xie, Renyao Pan, YaoYifu Yu, Ruiheng Wang, Huijian Zheng, Guorui Huang, Han Liu

“…Clinical characteristics, the tumor immune microenvironment, and drug sensitivity differences between the high- and low-risk groups were also analyzed. …”
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Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional studyResearch in... by Cristina Saldivia-Siracusa, Eduardo Santos Carlos de Souza, Arnaldo Vitor Barros da Silva, Anna Luíza Damaceno Araújo, Caíque Mariano Pedroso, Tarcília Aparecida da Silva, Maria Sissa Pereira Sant'Ana, Felipe Paiva Fonseca, Hélder Antônio Rebelo Pontes, Marcos G. Quiles, Marcio Ajudarte Lopes, Pablo Agustin Vargas, Syed Ali Khurram, Alexander T. Pearson, Mark W. Lingen, Luiz Paulo Kowalski, Keith D. Hunter, André Carlos Ponce de Leon Ferreira de Carvalho, Alan Roger Santos-Silva

“…Transfer learning strategies enhanced performance for both algorithms, and the greatest model achieved mean accuracy, precision, recall, F1-score and AUROC of 0.799, 0.837, 0.756, 0.794 and 0.863 during internal testing, respectively. …”
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Technical validation of real-world monitoring of gait: a multicentric observational study by Sarah Koch, Clint Hansen, Walter Maetzler, Anne-Elie Carsin, Kristin Taraldsen, Kamiar Aminian, Clemens Becker, Lorenzo Chiari, Anisoara Paraschiv-Ionescu, Jorunn L Helbostad, Beatrix Vereijken, Lynn Rochester, Philip Brown, David Singleton, Basil Sharrack, Brian Caulfield, Ellen Buckley, Claudia Mazza, Nikolaos Chynkiamis, Felix Kluge, M Encarna Micó-Amigo, Francesca Salis, Lars Schwickert, Kirsty Scott, Ioannis Vogiatzis, Alison Yarnall, Alison Keogh, Silvia Del Din, Björn Eskofier, Lisa Alcock, Stefano Bertuletti, Tecla Bonci, Marina Brozgol, Marco Caruso, Andrea Cereatti, Fabio Ciravegna, Jordi Evers, Eran Gazit, Jeffrey M Hausdorff, Hugo Hiden, Emily Hume, Neil Ireson, Cameron Kirk, Arne Küderle, Vitaveska Lanfranchi, Arne Mueller, Isabel Neatrour, Martijn Niessen, Luca Palmerini, Lucas Pluimgraaff, Luca Reggi, Henrik Sillen, Abolfazi Soltani, Martin Ullrich, Linda Van Gelder, Elke Warmerdam

“…Data and algorithms will be made publicly available.Trial registration number ISRCTN (12246987).…”
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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…The model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). …”
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Mortality impact, risks, and benefits of general population screening for ovarian cancer: the UKCTOCS randomised controlled trial by Usha Menon, Aleksandra Gentry-Maharaj, Matthew Burnell, Andy Ryan, Jatinderpal K Kalsi, Naveena Singh, Anne Dawnay, Lesley Fallowfield, Alistair J McGuire, Stuart Campbell, Steven J Skates, Mahesh Parmar, Ian J Jacobs

“…Interventions One of two annual screening strategies: (1) multimodal screening (MMS) using a longitudinal CA125 algorithm with repeat CA125 testing and transvaginal scan (TVS) as second line test (2) ultrasound screening (USS) using TVS alone with repeat scan to confirm any abnormality. …”
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Inter-Annotator Agreement and Its Reflection in LLMs and Responsible AI. by Amir Toliyat, Elena Filatova, Ronak Etemadpour

“… Recent research on Responsible AI, particularly in addressing algorithmic biases, has gained significant attention. …”
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Forecasting readmission in COVID-19 patients utilizing blood biomarkers and machine learning in the Hospital-at-Home program by Maria Glòria Bonet-Papell, Maria Glòria Bonet-Papell, Georgina Company-Se, María Delgado-Capel, Beatriz Díez-Sánchez, Lourdes Mateu-Pruñosa, Roger Paredes-Deirós, Jordi Ara del Rey, Lexa Nescolarde

“…Various classification algorithms (bagged trees, KNN, LDA, logistic regression, Naïve Bayes, and the support vector machine [SVM]) were implemented to predict readmission, with performance evaluated using accuracy, sensitivity, specificity, F1 score, and the Matthews Correlation Coefficient (MCC).ResultsSignificant differences were observed in IL-6, Hs-TnT, CRP (p < 0.001), and ferritin (p < 0.01) between the first day of conventional hospitalization and the first day of HaH for patients who were not readmitted. …”
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Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage by Weizhi Qiu, Penglei Cui, Shaojie Li, Zhenzhou Tang, Jiani Chen, Jiayin Wang, Yasong Li

“…Five machine learning algorithms were used to construct the prediction model and the model accuracy was evaluated by ROC curves. …”
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Prediction of Early Diagnosis in Ovarian Cancer Patients Using Machine Learning Approaches with Boruta and Advanced Feature Selection by Tuğçe Öznacar, Tunç Güler

“…Random Forest and CatBoost’s performances demonstrated significant differences in contrast to other algorithms (respectively, AUC 0.94% and 0.95%). …”
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