Exploring the impact of deubiquitination on melanoma prognosis through single-cell RNA sequencing by Su Peng, Jiaheng Xie, Xiaohu He

“…Cells were categorized into DUB_high and DUB_low groups based on AUCell scores, followed by differential expression analysis. Importantly, we constructed a robust prognostic model utilizing various genes, which was evaluated in the TCGA cohort and an external validation cohort.ResultsOur prognostic model, developed using Random Survival Forest (RSF) and Ridge Regression methods, demonstrated excellent predictive performance, evidenced by high C-index and AUC values across multiple cohorts. …”
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Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment by Chuanyu Ma, Chuanyu Ma, Guandu Li, Xiaohan Song, Xiaochen Qi, Tao Jiang

“…The ITH-score of PRAD samples was evaluated using the DEPTH algorithm. The optimal cut-off value of RiskScore was calculated based on the difference in survival curves, and PRAD patients were classified into high ITH and low ITH groups based on the optimal cut-off value. …”
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Advancements in Parkinson’s Disease Diagnosis: A Comprehensive Survey on Biomarker Integration and Machine Learning by Ruchira Pratihar, Ravi Sankar

“…This comprehensive review explores the advancements in machine learning algorithms in the diagnosis of Parkinson’s disease (PD) utilizing different biomarkers. …”
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Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning by Xiao Zeng, Qiong Ma, Chun-Xia Huang, Jun-Jie Xiao, Xi Fu, Yi-Feng Ren, Yu-Li Qu, Hong-Xia Xiang, Mao Lei, Ru-Yi Zheng, Yang Zhong, Ping Xiao, Xiang Zhuang, Feng-Ming You, Jia-Wei He

“…Saliva samples were subjected to sequencing of the V3–V4 region of the 16S rRNA gene to assess microbial diversity and differential abundance. Seven advanced machine learning algorithms (logistic regression, support vector machine, multi-layer perceptron, naïve Bayes, random forest, gradient boosting decision tree, and LightGBM) were utilized to evaluate performance and identify key microorganisms, with fivefold cross-validation employed to ensure robustness. …”
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Lipids as key biomarkers in unravelling the pathophysiology of obesity-related metabolic dysregulation by Anis Adibah Osman, Siok-Fong Chin, Lay-Kek Teh, Noraidatulakma Abdullah, Nor Azian Abdul Murad, Rahman Jamal

“…Metabolites were identified using a molecular feature and chemical formula algorithm, followed by a differential analysis using MetaboAnalyst 5.0. …”
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Identification of biomarkers and immune microenvironment associated with heart failure through bioinformatics and machine learning by Jingyun Jin, Jingyun Jin, Shuyan Qin, Qiang Fu, Changzhi Yu, Hongjin Wu, Hongjin Wu, Hongjin Wu

“…Single Sample Gene Set Enrichment Analysis and CIBERSORT algorithm were applied to evaluate immune cell infiltration in HF and its relationship with hub genes.ResultsDifferential analysis identified 165 differentially expressed genes (DEGs), and WGCNA revealed the “blue” module showing a significant correlation with HF. …”
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COPING STRATEGIES IN PATIENTS WITH PROSTATE CANCER by J. R. Gardanova, I. I. Abdullin, D. N. Chernov, A. V. Chernov, Y. I. Kekteeva

“…All men were subjected to the standard algorithm for the evaluation of hormonal status, the PSA, taking a history, inspection and physical examination, magnetic resonance imaging and scintigraphy of bones of a skeleton. …”
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Predicting Severe Knee Arthritis Based on Two Inertial Measurement Unit Sensors as a Dynamic Coordinate System Using Classical Machine Learning by Erfan Azizi, Mohammadsadegh Darbankhalesi, Amirhossein Zare, Zahra Sadat Rezaeian, Saeed Kermani

“…The purpose of this study was to evaluate the ability of a wearable device to differentiate between healthy individuals and those with severe osteoarthritis (grade 4). …”
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Augmented robustness in home demand prediction: Integrating statistical loss function with enhanced cross-validation in machine learning hyperparameter optimisation by Banafshe Parizad, Ali Jamali, Hamid Khayyam

“…Using three evolutionary algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) we optimize two ensemble models: XGBoost and LightGBM. …”
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Exploration of the shared gene signatures and molecular mechanisms between cardioembolic stroke and ischemic stroke by Xuan Wang, Xuan Wang, Xueyuan Liu, Xueyuan Liu

“…Three machine learning algorithms were employed to detect biomarkers from the core shared genes, and the diagnostic value of the hub genes was evaluated by establishing a predictive nomogram. …”
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Prognosis of unfavorable course of ulcerative colitis as indication for anticytokine therapy by A. Yu. Baranovsky, E. A. Kondrashina, A. G. Kharitonov

“…Prognostic system for various variants of course of UC and its complications, 27 most significant clinical signs of disease based on differentiated clinical and statistical evaluation was submitted. …”
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MANAGEMENT OF PATIENTS WITH CERVICAL INSUFFICIENCY by S. A. Levakov, E. I. Borovkova, N. A. Sheshukova, I. M. Borovkov

“…In identifying ICN after 24 weeks, the method of choice is the appointment of micronized progesterone and the installation of an unloading obstetric pessary. The use of a differentiated algorithm of management of patients with ICN reduces the chance of unexpected premature birth and neonatal morbidity and mortality.…”
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Estimation of the Time of Occurrence of the Maximum Electrical Demand by Selecting the Optimal Classification Model and Making Use of Unbalanced Data by César Aristóteles Yajure, Valesca M. Fuenzalida Sánchez

“… Studies on electricity demand forecasting usually focus on the magnitude of the variable, however, the methodology used in this study also addresses the time at which the peak demand occurs, crucial for planning energy generation, smoothing the demand peaks and establishing differentiated rates. To predict the time of maximum demand, supervised machine learning algorithms were used: random forests, K nearest neighbors, support vector machine, and logistic regression. …”
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Towards the digital extraction column: Online-monitoring and analysis of fluid dynamics in liquid-liquid extraction columns by Andreas Palmtag, Lorenz Lehmann, Leon Rojas Hanz, Uliana Kiseleva, Andreas Jupke

“…The camera images were processed by YOLOv8 for drop detection and the ByteTrack algorithm for drop tracking, achieving high accuracy on unseen data. …”
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Classification of Fungal Pigments by Simulating Their Optical Properties Using Evolutionary Optimization by Roman Y. Pishchalnikov, Denis D. Chesalin, Vasiliy A. Kurkov, Andrei P. Razjivin, Sergey V. Gudkov, Andrey A. Grishin, Alexey S. Dorokhov, Andrey Yu. Izmailov

“…Based on the differential evolution algorithm and semiclassical quantum simulations, we have recently proposed a method for classifying and analyzing the optical properties of organic pigments. …”
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Transcriptional landscape of pleural mesothelioma patients in relation to NF2 gene mutational status by Carlos Orozco-Castaño, Alejandro Mejía-Garcia, Hsuan Megan Tsao, Diego A. Bonilla, Carlos Carvajal-Fierro, Ricardo Bruges-Maya, Alba Combita, Rafael Parra-Medina

“…Logistic regression was used to evaluate associations with clinical variables, while transcriptomic differences were examined through differential expression and functional enrichment analyses. …”
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CropsDisNet: An AI-Based Platform for Disease Detection and Advancing On-Farm Privacy Solutions by Mohammad Badhruddouza Khan, Salwa Tamkin, Jinat Ara, Mobashwer Alam, Hanif Bhuiyan

“…The integration of a differential privacy algorithm into our CropsDisNet model could establish the benefits of automated crop disease classification without compromising on-farm data privacy by reducing training data leakage. …”
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Mendelian randomization and bioinformatics analysis of TRIM31 with chronic liver disease by YOU Junyi, LIANG Guoqiang, SONG Xiudao

“…The expression level of TRIM31 in HCC was analyzed using TCGA data, and the correlation between TRIM31 expression and immune cell infiltration was analyzed using the CIBERSORT algorithm. The diagnostic accuracy was evaluated by ROC curves. …”
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Comprehensive analysis of IRF8-related genes and immune characteristics in lupus nephritis by Zhibin Yu, Chenghui Zheng, Yilun Wang

“…Objective: We aimed to explore gene networks and potential biomarkers for LN by analyzing the GSE32591 and GSE113342 datasets from the Gene Expression Omnibus database, focusing on IRF8 and IRF8-related genes.MethodsWe used differential expression analysis, functional enrichment, protein-protein interaction (PPI) network construction, and the CIBERSORT algorithm for immune infiltration assessment. …”
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Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMS by Gaofeng Sun, Quan Wang, Xinjie Shan, Maierheba Kuerbanjiang, Ruiying Ma, Wensi Zhou, Lin Sun, Qifeng Li

“…Seven core differential metabolites were identified by the three machine learning algorithms. …”
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