DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure by Wenwu Tang, Wenwu Tang, Zhixin Wang, Xinzhu Yuan, Liping Chen, Haiyang Guo, Zhirui Qi, Ying Zhang, Xisheng Xie

“…In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.ResultsTotally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. Through GSEA analysis, it was found that the four genes were closely related to ribosome, cell cycle, ubiquitin-mediated proteolysis. …”
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Optimal vaccination model of airborne infection under variable humidity and demographic heterogeneity for hybrid fractional operator technique by Saima Rashid, Ilyas Ali, Nida Fatima, Tehreem Fatima, Fekadu Tesgera Agam, Sayed K. Elagan

“…Our system’s best-fit parameter settings were detected using the Markov Chain Monte Carlo (M-C-M-C) technique with influenza information collected in Spain. We predict a basic reproduction number of 1.3645 (96% C.I: (1.3644, 1.3646)). …”
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At-Home Evaluation of Both Wearable and Touchless Digital Health Technologies for Measuring Nocturnal Scratching in Atopic Dermatitis: Analytical Validation Study by Stefan Avey, Mark Morris, Davit Sargsyan, Molly V Lucas, Andrea O'Brisky, Kenneth Mosca, Andrew Elias, Nicholas Fountoulakis, Mehdi Boukhechba, Xuen Hoong Kok, Saiyam Jain, Mehrnoosh Oghbaie, Nikolay V Manyakov, Miao Wang, Zuleima Aguilar, Lynn Yieh

“…ObjectiveIn this study, we present the analytical validation of 2 DHTs: the GENEActiv wristband with Philips sleep and scratch algorithms (“Philips”) and the Emerald radio frequency touchless sensor (“Emerald”) to measure nocturnal scratching in adults with AD. …”
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Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis by Yuanbo Bi, Dongrui Li, Ruochen Pang, Chengxv Du, Da Li, Xiaoyv Zhao, Haitao Lv

“…Inclusion criteria were studies involving PDAC patients, using deep learning algorithms for diagnosis evaluation, using histopathological results as the reference standard, and having sufficient data. …”
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Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis by Yang M, Zhang X, Zhou C, Du Y, Zhou M, Zhang W

“…Potential microRNA (miRNA)-messenger RNA (mRNA) and miRNA-long non-coding RNA (lncRNA) interactions were predicted using miRanda and TargetScan tools.Results: We identified 381 DEGs (217 upregulated, 164 downregulated). …”
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Differential chemoproteomic analysis of RRS-1 candidate molecule and molecules of several nonsteroidal anti-inflammatory drugs by P. A. Galenko-Yaroshevsky, I. Yu. Torshin, A. N. Gromov, O. A. Gromova, K. F. Suzdalev, R. A. Murashko, A. V. Zelenskaya, A. V. Zadorozhniy, T. R. Glechyan, G. V. Simavonyan, E.M. I. Muhammad

“…Chemoproteomic modeling of pharmacological effects of RRS-1 molecule and a number of well-known NSAIDs (diclofenac, nimesulide, ketorolac) on human proteome was carried out on the basis of numerical prediction algorithms over the space of heterogeneous feature descriptions, developed in the topological approach to recognition by Yu.I. …”
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Relationship between stress hyperglycemia ratio and the incidence of atrial fibrillation in patients after coronary artery bypass grafting: a retrospective study based on the MIMIC... by Runjia Liu, Jiatong Li, Jing Zeng, Yuxuan Tao, Dong Chen, Haixia Li

“…We employed logistic regression models, restricted cubic splines (RCS), threshold effect analysis, ubgroup analysis, Boruta algorithm, lasso algorithm, and receiver operating characteristics (ROC) to analyze the relationship between SHR and POAF incidence comprehensively. …”
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The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis by Guanghua Shi, Xiaocui Shi, Meng Zhang, Rui Cheng, Mengqing Hu, Yu Zhao, Shimei Li, Xiuxiu Li, Haiyun Ma, Pengcui Li

“…Feature genes were further refined by intersecting results from three machine learning algorithms and constructing an artificial neural network (ANN). …”
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Single cell transcriptomic analysis reveals tumor immune infiltration by macrophage cells gene signature in lung adenocarcinoma by Xiaotong Guo, Youjun Deng, Wenjun Jiang, Heng Li, Yisheng Luo, Huachuan Zhang, Hao Wu

“…High TGS tumors exhibited enrichment in TGF-β signaling and hypoxia pathways, suggesting their potential utility in predicting prognosis and immune responses in patients with LUAD. …”
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Integrated Bioinformatics and Experimental Validation Reveal Macrophage Polarization-Related Biomarkers for Osteoarthritis Diagnosis by He Q, Liu L, Hu X, Lin L, Song Z, Xia Y, Lin Q, Wei J, Li S

“…Least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to identify hub genes and construct a diagnostic model validated through internal datasets and multiple external bulk RNA-seq and single-cell RNA-seq data. …”
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Development of a Diagnostic Model for Focal Segmental Glomerulosclerosis: Integrating Machine Learning on Activated Pathways and Clinical Validation by Ge Y, Liu X, Shu J, Jiang X, Wu Y

“…We then developed a highly accurate diagnostic model by integrating nine machine learning algorithms into 101 combinations, achieving near-perfect AUC values across training, validation, and external cohorts. …”
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ATP6V0A4 as a novel prognostic biomarker and potential therapeutic target in oral squamous cell carcinoma by Xiaopu Gao, Jiamin Zhou, Yu Qiao, Chuyin Lin, Guanxiong Zhang, Qiuyu Wu, Zhikang Su, Qianji Zhang, Songkai Huang

“…Further, immunotherapy response prediction models and drug sensitivity analysis were utilized to define therapeutic potential. …”
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Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models by Venkat U.C. Bodempudi, Guoming Li, J. Hunter Mason, Jeanna L. Wilson, Tianming Liu, Khaled M. Rasheed

“…The DLM framework included a bird detection model, data filtering algorithms based on mating duration, and logic frameworks for mating identification based on bird count changes. …”
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Genetic variants of the DLK1, KISS1R, MKRN3 genes in girls with precocious puberty by E. A. Sazhenova, O. Yu. Vasilyeva, E. A. Fonova, M. B. Kankanam Pathiranage, A. Yu. Sambyalova, E. E. Khramova, L. V. Rychkova, S. A. Vasilyev, I. N. Lebedev

“…The pathogenicity of identified genetic variants and the functional significance of the protein synthesized by them were analyzed according to recommendations for interpretation of NGS analysis results using online algorithms for pathogenicity prediction (Variant Effect Predictor, Franklin, Varsome, and PolyPhen2). …”
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A capable numerical scheme for solving nonlinear Volterra delay integral equations of the third kind by Rohollah Ghaedi Ghalini, Esmail Hesameddini, Hojatollah Laeli Dastjerdi

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Race course characteristics are the most important predictors in 48 h ultramarathon running by Beat Knechtle, David Valero, Elias Villiger, Katja Weiss, Pantelis T. Nikolaidis, Lorin Braschler, Rodrigo Luiz Vancini, Marilia Santos Andrade, Ivan Cuk, Thomas Rosemann, Mabliny Thuany

“…A machine learning (ML) model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, where the race occurs and race course characteristic such as elevation (flat or hilly) and surface (asphalt, cement, granite, grass, gravel, sand, track, or trail). …”
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Research and application of intelligent learning path optimization based on LSTM-Transformer model by Jinling Wang, Wandong Chai

“…In the global wave of digital learning, how to optimize personalized learning paths and improve learning efficiency has become a key issue to be solved urgently in the field of education. Based on this, this study proposes a hypothesis: the intelligent learning path optimization strategy based on the LSTM-Transformer model can achieve accurate prediction and personalized optimization of learners' learning paths with the help of deep learning technology. …”
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Multi-scenario analysis of green water resource efficiency under carbon emission constraints in the Chengdu-Chongqing urban agglomeration, China: A system dynamics approach by Keyao Yu, Zhigang Li

“…Five scenarios are then set up: baseline development, economic priority, green innovation, water-saving and emission reduction, and comprehensive development. These scenarios are used to simulate and predict different development paths and evaluate the impact of various key indicators on GWRE. …”
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Use of artificial intelligence to support prehospital traumatic injury care: A scoping review by Jake Toy, Jonathan Warren, Kelsey Wilhelm, Brant Putnam, Denise Whitfield, Marianne Gausche‐Hill, Nichole Bosson, Ross Donaldson, Shira Schlesinger, Tabitha Cheng, Craig Goolsby

“…The most common study objectives were to predict the need for critical care and life‐saving interventions (29%), assist in triage (22%), and predict survival (20%). …”
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Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile by Lien Rodríguez-López, Lisandra Bravo Alvarez, Iongel Duran-Llacer, David E. Ruíz-Guirola, Samuel Montejo-Sánchez, Rebeca Martínez-Retureta, Ernesto López-Morales, Luc Bourrel, Frédéric Frappart, Roberto Urrutia

“…This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. …”
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