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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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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Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration by Maoqiang Lin, Maoqiang Lin, Shaolong Li, Yabin Wang, Yabin Wang, Guan Zheng, Guan Zheng, Fukang Hu, Fukang Hu, Qiang Zhang, Qiang Zhang, Pengjie Song, Haiyu Zhou, Haiyu Zhou

“…The receiver operating characteristic (ROC) curve, nomogram, and Decision Curve Analysis (DCA) were used to evaluate the model effect. In addition, we constructed a potential drug regulatory network and competitive endogenous RNA (ceRNA) network for key LAGs.ResultsA total of 15 differentially expressed LAGs were identified. …”
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Identification and quantification of transposable element transcripts using Long-Read RNA-seq in Drosophila germline tissues by Rebollo, Rita, Gerenton, Pierre, Cumunel, Eric, Mary, Arnaud, Sabot, François, Burlet, Nelly, Gillet, Benjamin, Hughes, Sandrine, S. Oliveira, Daniel, Goubert, Clément, Fablet, Marie, Vieira, Cristina, Lacroix, Vincent

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Topology aware multitask cascaded U-Net for cerebrovascular segmentation. by Pierre Rougé, Nicolas Passat, Odyssée Merveille

“…This loss requires computing the skeletons of both the manual annotation and the predicted segmentation in a differentiable way. Currently, differentiable skeletonization algorithms are either inaccurate or computationally demanding. …”
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Computed tomography enterography radiomics and machine learning for identification of Crohn’s disease by Qiao Shi, Yajing Hao, Huixian Liu, Xiaoling Liu, Weiqiang Yan, Jun Mao, Bihong T. Chen

“…A machine learning classification system was constructed by combining six selected radiomics features with eight classification algorithms. The models were trained using leave-one-out cross-validation and evaluated for accuracy. …”
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Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic... by Peng Lyu, Na Xie, Xu-peng Shao, Shuai Xing, Xiao-yue Wang, Li-yun Duan, Xue Zhao, Jia-min Lu, Rong-fei Liu, Duo Zhang, Wei Lu, Kai-liang Fan

“…Overall, we detected 12 differentially expressed genes. CD177, MCEMP1, MMP8, and OLAH were examined as diagnostic indicators for pediatric septic shock, revealing statistically significant differences (P < 0.01) and diagnostic efficacy in the validation cohort. …”
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Assessment of copeptin and obestatin levels in coronary artery disease patients by Saraa Ali, Fatma F. Abdel Hamid, Samia Hussein, Shaimaa Wageeh, Doaa M. Ibrahim

“…They are currently assessed clinically through myocardial enzymes, electrocardiography, risk score algorithms, and angiography. It is important to find a suitable marker to differentiate them from other causes of chest pain. …”
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Comparison of 46 Cytokines in Peripheral Blood Between Patients with Papillary Thyroid Cancer and Healthy Individuals with AI-Driven Analysis to Distinguish Between the Two Groups by Kyung-Jin Bae, Jun-Hyung Bae, Ae-Chin Oh, Chi-Hyun Cho

“…Among the five classification algorithms evaluated, XGBoost demonstrated superior performance in terms of accuracy, precision, sensitivity (recall), specificity, F1-score, and ROC-AUC score. …”
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High resolution tissue and cell type identification via single cell transcriptomic profiling. by Muyi Liu, Suilan Zheng, Hongmin Li, Bruce Budowle, Le Wang, Zhaohuan Lou, Jianye Ge

“…By incorporating a crucial and unique reference cell quality differentiation phase of targeting only high confident cells as reference, scTissueID achieved better and consistent performance in determining cell and tissue types compared to 8 state-of-art single cell annotation pipelines and 6 widely adopted machine learning algorithms, as demonstrated through a large-scale and comprehensive comparison study using both forensic-relevant and Human Cell Atlas (HCA) data. …”
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Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China by Aiping Gou, Xuyuan Wang, Jiangbo Wang, Chenjie Wang, Guanzheng Tan

“…This study investigates the spatial heterogeneity of the GVI and its influencing factors in Yuzhong District, Chongqing, a mountainous city in China. Deep learning algorithms were employed to calculate the green visibility of street view images, and Geographic Weighted Regression (GWR) and the Optimal Parameter-Based Geodetector (OPGD) were utilized to analyze the relationships between GVI and factors such as road physical attributes, the Normalized Difference Vegetation Index (NDVI), and topographic features. …”
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Radiomics Analysis of Whole-Kidney Non-Contrast CT for Early Identification of Chronic Kidney Disease Stages 1–3 by Guirong Zhang, Pan Zhang, Yuwei Xia, Feng Shi, Yuelang Zhang, Dun Ding

“…Key features were selected through Relief, MRMR, and LASSO regression algorithms. A machine learning classifier was trained to differentiate CKD from healthy kidneys and compared with the radiologist assessments. …”
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RETRACTED ARTICLE: Molecular mechanism and diagnostic marker investigation of endoplasmic reticulum stress on periodontitis by Qianqian Sun, Enqiang Zhu

“…Two machine learning algorithms were used to reveal potential ERS diagnostic markers of periodontitis. …”
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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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Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma by Xiang Li, Donghao Yin, Jiahao Geng, Yanyu Xu, Zijing Xu, Xuemeng Yang, Quanwei Li, Zimeng Shang, Zhiyun Yang, Zhong Xu, Jiabo Wang, Enxiang Zhang, Xinhua Song

“…Based on prognosis-related DEGs, patients and cells were stratified into high- and low-expression groups using corresponding computational algorithms. The intersecting DEGs from both datasets were analyzed using univariate Cox regression, and a prognostic risk score model was constructed through machine learning algorithms. …”
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