Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning by Haoran Xie, Junjun Wang, Qiuyan Zhao

“…Results: We successfully identified seven signature MRDEGs, including CYP7A1, GCK, AKR1B10, HPRT1, GPD1, FADS2, and ENO3, through PPI network analysis and machine learning algorithms. The gene model displayed exceptional diagnostic performance in the training and validation cohorts, as evidenced by the area under ROC curve (AUC) exceeding 0.9. …”
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The Field Attributes May not Accurately Predict the Need for Early Tracheostomy Tube Insertion in Severe TBI Patients: A New Insight With the Help of AI Algorithms by Adrina Habibzadeh, Sepehr Khademolhosseini, Reza Taheri, Amin Niakan, Saeed Tayebi Khorami, Hadis Ghasemi, HosseinAli Khalili

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Predicting the risk of gastroparesis in critically ill patients after CME using an interpretable machine learning algorithm – a 10-year multicenter retrospective study by Yuan Liu, Songyun Zhao, Wenyi Du, Wei Shen, Ning Zhou

“…The clinical data of critically ill patients transferred to the intensive care unit (ICU) post-CME were meticulously analyzed to identify key risk factors associated with the development of gastroparesis.MethodsWe gathered 34 feature variables from a cohort of 1,097 colon cancer patients, including 87 individuals who developed gastroparesis post-surgery, across multiple hospitals, and applied a range of machine learning algorithms to construct the predictive model. …”
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Identification of GABBR2 as a diagnostic marker and its association with Aβ in Alzheimer's disease by Huijun Li, Yawei Fan, Chan Chen, Yuzhong Xu, Xiong Wang, Wei Liu

“…The overlapped hub genes were further processed using machine learning algorithms, intersected with module gene from protein-protein interaction (PPI) network constructed with DEGs, to yield co-hub genes. …”
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Association between pro-inflammatory diet and fecal incontinence: a large population-based study by Haiyang Wang, Haiyang Wang, Zihan Liu, Zihan Liu, Xingfu Lu, Enyu Luan, Enyu Luan, Tingting Dong, Tingting Dong, Can Li, Can Li, Yanni Lv, Erkang Wu, Tao Shen, Tao Shen

“…Meanwhile, we identified key dietary factors for FI using multiple machine learning algorithms. Finally, we assessed the mediation role of inflammatory indicators on the association of key dietary factors with FI through mediation analysis.ResultsAfter adjustment for potential confounding variables, our results showed the highest tertile exhibited dramatically increasing prevalence of FI compared to the lower tertile (OR 1.27, 95% CI 1.06–1.53), suggesting a positive association between DII and FI. …”
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Assessing the association between ADHD and brain maturation in late childhood and emotion regulation in early adolescence by Kristóf Ágrez, Pál Vakli, Béla Weiss, Zoltán Vidnyánszky, Nóra Bunford

“…Whether the difference between an individual’s brain age predicted by machine-learning algorithms trained on neuroimaging data and that individual’s chronological age, i.e. brain-predicted age difference (brain-PAD) predicts differences in emotion regulation, and whether ADHD problems add to this prediction is unknown. …”
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Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods by Yihao Yu, Yuqi Yang, Qian Li, Jing Yuan, Yan Zha

“…Abstract Early and accurate identification of patients at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent and improve prognosis potentially. …”
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Machine learning-based prognostic prediction model of pneumonia-associated acute respiratory distress syndrome by Jing Lv, Juan Chen, Meijun Liu, Xue Dai, Wang Deng

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Identification of Exosome-Associated Biomarkers in Diabetic Foot Ulcers: A Bioinformatics Analysis and Experimental Validation by Tianbo Li, Lei Gao, Jiangning Wang

“…Support vector machine–recursive feature elimination (SVM-RFE) and the Boruta random forest algorithm distilled five biomarkers (DIS3L, EXOSC7, SDC1, STX11, SYT17). …”
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ProstaNet: A Novel Geometric Vector Perceptrons–Graph Neural Network Algorithm for Protein Stability Prediction in Single- and Multiple-Point Mutations with Experimental Validation... by Tianjian Liang, Ze-Yu Sun, Rieko Ishima, Xiang-Qun Xie, Ying Xue, Wei Li, Zhiwei Feng

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A clustering-based approach for classifying data streams using graph matching by Yuxin Du, Mingshu He, Xiaojuan Wang

“…This allows for associating clusters in the test network with clusters in the initial network, enabling the labeling of test clusters based on associated clusters in the training set. …”
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Identification of hub genes for the diagnosis associated with heart failure using multiple cell death patterns by Hua‐jing Yuan, Hui Yu, Yi‐ding Yu, Xiu‐juan Liu, Wen‐wen Liu, Yi‐tao Xue, Yan Li

“…DHRS11 and LRKK2 were identified as PCD‐associated HF hub genes by machine learning algorithms. …”
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Using a machine learning algorithm and clinical data to predict the risk factors of disease recurrence after adjuvant treatment of advanced-stage oral cavity cancer by Sheng-Yao Huang, Ren-Jun Hsu, Dai-Wei Liu, Wen-Lin Hsu

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Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept stu... by Lorenzo Ruinelli, Pietro Cippà, Chantal Sieber, Clelia Di Serio, Paolo Ferrari, Antonio Bellasi

“…The database was divided into training and validation sets. Machine Learning (ML) algorithms were developed with 10-fold cross-validation, and diagnostic accuracy was evaluated.…”
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Integrated single-cell and transcriptome sequencing to construct a prognostic model of M2 macrophage-related genes in prostate cancer by TANG Kairan, FENG Chengling, HAN Bangmin

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Correlation Between Depression-Associated Genes and Cancer Types: Predicting Cancer Based on Mutation Frequencies by Fernando Patricio Carranco-Avila, Clayanela Zambrano-Caicedo, Jonathan Javier Loor-Duque, Ariana Deyaneira Jimenez-Narvaez, Ivan Galo Reyes-Chacon, Paulina Vizcaino, Isidro Rafael Amaro Martin, Manuel Eugenio Morocho-Cayamcela

“…The analysis employed advanced methodologies, including HJ biplot K-means and DBSCAN clustering algorithms for pattern grouping in 2D. This process generated a dataset, enabling the training and testing of machine learning and deep learning classification models. …”
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FGFR2 identified as a NETs-associated biomarker and therapeutic target in diabetic foot ulcers by Linrui Dai, Shunli Rui, Mengling Yang, Shiyan Yu, Qingqing Chen, Hongyan Wang, Bo Deng, Liling Deng, Wei Hao, Xiaohua Wu, David G. Armstrong, Zhidong Cao, Xiaodong Duan, Wuquan Deng

“…These DEGs were intersected with a NETs-related gene set to identify NETs-associated DEGs (NETDEGs). LASSO logistic regression and Random Forest algorithms were applied to the NETDEGs to select key feature genes. …”
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Using Interpretable Artificial Intelligence Algorithms in the Management of Blunt Splenic Trauma: Applications of Optimal Policy Trees as a Treatment Prescription Aid to Improve Pa... by Vahe S. Panossian, Yu Ma, Bolin Song, Jefferson A. Proaño-Zamudio, Veerle P. C. van Zon, Ikemsinachi C. Nzenwa, Azadeh Tabari, George C. Velmahos, Haytham M. A. Kaafarani, Dimitris Bertsimas, Dania Daye

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Ureteroscopy for Urolithiasis in Pregnancy: Outcomes of a Multicentre Study on Behalf of the European Association of Urology Section of Endourology by Patrick Juliebø-Jones, Vineet Gauhar, Smita De, Øyvind Ulvik, Yiloren Tanidir, Nebil Akdogan, Cemil Aydin, Pablo Contreras, Maria Florencia Frascheri, Javier Pizzarello, Deepak Ragoori, Lazaros Tzelves, Peder Gjengstø, Mathias Sørstrand Æsøy, Albert El Hajj, Elsayed Desouky, Alejandro Bautista-Perez-Gavilan, Vigen Malkhasyan, Olivier Traxer, Christian Beisland, Bhaskar K. Somani

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A Semi-Supervised Machine Learning Approach Using K-Means Algorithm to Prevent Burst Header Packet Flooding Attack in Optical Burst Switching Network by Patwary et al.

“…In this study, we propose a semi-supervised machine learning approach using k-means algorithm, to detect malicious nodes in an OBS network. …”
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