Identification of marker genes associated with oxidative stress in hypertrophic cardiomyopathy using bioinformatics analysis and experimental validation by Jian Zhuo, Ding Ding, Mengkang Fan, Weiwei Lu, Xiaochen Lu, Lihong Yao, Hongzhuan Sheng

“…GSE36961 and GSE141910 were downloaded from the Gene expression Omnibus (GEO) database, and genes associated with oxidative stress were searched in the Gene Ontology (GO) database. After differential analysis, marker genes were obtained using LASSO and SVM-RFE algorithms. …”
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Molecular insights into ulcerative colitis and orbital inflammation by Kang Tan, Pei Liu, Zixuan Wu, Xi Long, Yunfeng Yu, Pengfei Jiang, Qinghua Peng

“…Machine learning algorithms identified eight potential genes for diagnostic and therapeutic use, with CXCL10 emerging as a key player in the pathogenesis of NSOI and UC. …”
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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…Univariate analysis was used to examine differential indicators, and variable selection was conducted through LASSO regression. …”
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S100A7 as a predictive biomarker in malignant transformation of oral epithelial dysplastic lesions by Jeffrey Soparlo, Lachlan McLean, Christina McCord, Linda Jackson-Boeters, Michael Shimizu, Michael Robinson, Wanninayake M. Tilakaratne, Mark R. Darling

“…The objective of this study was to evaluate S100A7 expression in OPMD which transformed and to correlate these results with the 3-tier and 2-tier dysplasia grading systems, and an S100A7 immunohistochemistry-based signature algorithm (S100A7 ARS). …”
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Integrative bioinformatics analysis identifies METTL1 as a regulator of immune infiltration and prognosis in breast cancer by Xiao-bin Lin, Yu-zhi Yao, Hong-min Ma, Cai-rong Zhu, Qi-rong Wen

“…Co-expressed genes and differentially expressed genes related to METTL1 were identified using Pearson correlation and Wilcoxon tests, with overlapping genes subjected to protein-protein interaction network construction to identify hub genes via degree algorithm. …”
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An Explainable CatBoost Model for Crater Classification Based on Digital Elevation Model by Minghao Zhu, Jialong Lai, Xiaoping Zhang, Yi Xu, Weidong He

“…In this study, we propose a machine-learning-based approach using the CatBoost algorithm to classify craters based on variables extracted from Digital Elevation Model (DEM) data. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
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DMRIntTk: Integrating different DMR sets based on density peak clustering. by Wenjin Zhang, Wenlong Jie, Wanxin Cui, Guihua Duan, You Zou, Xiaoqing Peng

“…In DMRIntTk, the genome is segmented into bins, and the reliability of each DMR set at different methylation thresholds is evaluated. Then, the bins are weighted based on the covered DMR sets and integrated into final DMRs using a density peak clustering algorithm. …”
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Building an Otoscopic screening prototype tool using deep learning by Devon Livingstone, Aron S. Talai, Justin Chau, Nils D. Forkert

“…The aim of this work was to develop and evaluate an automatic software prototype to identify otologic abnormalities using a deep convolutional neural network. …”
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Pediatric sepsis phenotypes for enhanced therapeutics: An application of clustering to electronic health records by Ioannis Koutroulis, Tom Velez, Tony Wang, Seife Yohannes, Jessica E. Galarraga, Joseph A. Morales, Robert J. Freishtat, James M. Chamberlain

“…Each segmentation was evaluated for phenotypic homogeneity, separation, and clinical use. …”
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Power Grid Clock Synchronization Optimization Based on Stackelberg Game Theory by Jiahao Li, Yitao Zhao, Yiming Zhang, Zhiyu Xia, Chuanxu Chen, Quansheng Guan

“…Furthermore, the network simulator Mininet and the open-source software-defined network framework Ryu are used to evaluate the performance of the proposed method. Compared with the bandwidth reservation scheme, our proposed method is more suitable for differentiated clock synchronization of resource-limited new energy power networks. …”
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Population-Level Cell Trajectory Inference Based on Gaussian Distributions by Xiang Chen, Yibing Ma, Yongle Shi, Yuhan Fu, Mengdi Nan, Qing Ren, Jie Gao

“…CPvGTI utilizes a Gaussian mixture model, optimized by the Expectation–Maximization algorithm, to construct new cell populations in the original data space. …”
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DS4NN: Direct training of deep spiking neural networks with single spike-based temporal coding by Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh

“…Backpropagation is the foremost prevalent and common algorithm for training conventional neural networks with deep construction. …”
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Eosinophilic Fasciitis: A Review of Research and Therapeutic Possibilities by Julia Biernikiewicz, Anna Wilewska, Bartosz Pomirski, Agata Pomirska, Milena Biernikiewicz, Konstanty Alabrudziński, Agnieszka Borowiec, Kinga Borowiec, Paulina Kwaśniewska, Patryk Graczyk

“…Given the rarity and complexity of EF, the study also seeks to clarify its differentiation from similar diseases and offer insights into effective therapies for early diagnosis and improved treatment outcomes. …”
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THE OBJECTIFICATION OF THE PRINCIPLE OF FAIRNESS IN TAX LAW OF RUSSIA IN THE ASPECT OF ANALYSIS OF RECENT LEGISLATIVE DEVELOPMENTS by M. Koilova

“…We also analyzed the modified algorithm of taxation of citizens' property and noted the problems of its practical approbation. …”
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Active Traffic Sensor Location Problem for the Uniqueness of Path Flow Identification in Large-Scale Networks by Ahmed Almutairi, Mahmoud Owais

“…Therefore, finding an efficient solution algorithm for large-scale networks is challenging. …”
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Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach by John Blake, Abu Saleh Musa Miah, Krzysztof Kredens, Jungpil Shin

“…This paper presents a novel algorithm that leverages cutting-edge machine-learning techniques to accurately and efficiently detect AI-generated texts. …”
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FW-S3KIFCM: Feature Weighted Safe-Semi-Supervised Kernel-Based Intuitionistic Fuzzy C-Means Clustering Method by Shirin Khezri, Nasser Aghazadeh, Mahdi Hashemzadeh, Amin Golzari Oskouei

“…On the other hand, existing SSC techniques fail to differentiate between the varied attributes within the feature space. …”
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Riemannian Manifolds for Biological Imaging Applications Based on Unsupervised Learning by Ilya Larin, Alexander Karabelsky

“…Graph-based segmentation has a long history, e.g., the normalized cuts algorithm treated segmentation as a graph partitioning problem—but only recently have such ideas merged with deep learning in an unsupervised manner. …”
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Prognostic and therapeutic potential of disulfidptosis-related genes in colon adenocarcinoma: a comprehensive multi-omics study by Ye Song, Haoran Zhu, Junyang Wei, Shanxue Yin

“…This study systematically evaluates their prognostic significance and explores their potential for therapeutic intervention in colon adenocarcinoma. …”
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