A Method Based on CNN–BiLSTM–Attention for Wind Farm Line Fault Distance Prediction by Ming Zhang, Qingzhong Gao, Baoliang Liu, Chen Zhang, Guangkai Zhou

“…The research results show that, compared with the random forest algorithm, decision tree algorithm, CNN, and LSTM neural network, the proposed method significantly improved the location accuracy and is more suitable for the fault distance measurement requirements of collector lines in the complex environments of wind farms. …”
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Identification and Characterization of Genes Associated with Intestinal Ischemia-Reperfusion Injury and Oxidative Stress: A Bioinformatics and Experimental Approach Integrating Hig... by Xie Y, Yang M, Huang J, Jiang Z

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Causal role of the pyrimidine deoxyribonucleoside degradation superpathway mediation in Guillain-Barré Syndrome via the HVEM on CD4 + and CD8 + T cells by Xianghua Liu, Lingling Liu, Jiuchang Zhang

“…Additionally, we also executed the Bayesian Weighting algorithm for verification. Mendelian randomization (MR) analysis determined the protective effect of the pyrimidine deoxyribonucleoside degradation superpathway on GBS (IVW: P = 0.0019, OR = 0.4508). …”
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Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates by Manzar Masud, Aamir Mubashar, Shahid Iqbal, Hassan Ejaz, Saad Abdul Raheem

“…Additionally, two tree-based machine learning (ML) algorithms were used: random forest (RF) and decision tree (DT). …”
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Global trends in machine learning applications for single-cell transcriptomics research by Xinyu Liu, Zhen Zhang, Chao Tan, Yinquan Ai, Hao Liu, Yuan Li, Jin Yang, Yongyan Song

“…Research hotspots concentrated on random forest (RF) and deep learning models, showing transition from algorithm development to clinical applications (e.g., tumor immune microenvironment analysis). …”
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Optimal Active-Reactive Power Dispatch for Distribution Network With Carbon Trading Based on Improved Multi-Objective Equilibrium Optimizer Algorithm by Furong Tu, Sumei Zheng, Kuncan Chen

“…The IMOEO algorithm incorporates a Sobol sequence initialization method, dynamic adjustment factors, and particles that employ various evolutionary strategies based on the crowding distance. …”
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Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments by Yonghua Pang, Jiahui Liang, Jiahui Liang, Yakai Deng, Weinan Chen, Yunyan Shen, Jing Li, Xin Wang, Zhiyao Ren

“…To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF).ResultsHOXC6 was identified as a key diagnostic biomarker for ES. …”
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Identification Exploring the Mechanism and Clinical Validation of Mitochondrial Dynamics-Related Genes in Membranous Nephropathy Based on Mendelian Randomization Study and Bioinfor... by Qiuyuan Shao, Nan Li, Huimin Qiu, Min Zhao, Chunming Jiang, Cheng Wan

“…<b>Methods:</b> Comprehensive bioinformatics analyses—encompassing Mendelian randomization, machine-learning algorithms, and single-cell RNA sequencing (scRNA-seq)—were employed to interrogate transcriptomic datasets (GSE200828, GSE73953, and GSE241302). …”
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A hybrid optimization algorithm based on cascaded (1 + PI)-PI-PID controller for load frequency control in interconnected power systems by Md. Shahid Iqbal, Md. Faiyaj Ahmed Limon, Md. Monirul Kabir, Md. Zakir Hossain, Md. Fahad Jubayer, Md. Janibul Alam Soeb

“…The algorithm's performance was assessed using four benchmark functions, revealing superior results compared to the Hybrid ABC-PSO algorithm. …”
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A novel color image encryption algorithm based on fractional-order conservative memristive hyperchaotic system and extended zig-zag transform by Fanqi Meng, Gang Wu, Juxiao Zhang

“…First, the dynamic characteristics of FCMHS were systematically studied and analyzed, and its potential application value in the field of information security is verified. The pseudo-random sequences generated by FCMHS passed all the tests of the National Institute of Standards and Technology (NIST). …”
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Integration of epigenomic and genomic data to predict residual feed intake and the feed conversion ratio in dairy sheep via machine learning algorithms by Pablo Augusto de Souza Fonseca, Aroa Suarez-Vega, Cristina Esteban-Blanco, Héctor Marina, Rocío Pelayo, Beatriz Gutiérrez-Gil, Juan-José Arranz

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Multi-objective flexible job-shop scheduling in hospital using discrete particle swarm optimization algorithm with adaptive inertia weight (DPSO-AIW) by Md. Limonur Rahman Lingkon, Adri Dash

“…Global selection based on the operation (GSO) of MA and random selection of OS are coupled in the initial population. …”
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In Infants with Neuroblastoma Standard Therapy Only Partially Reverts the Fecal Microbiome Dysbiosis Present at Diagnosis by Maria Valeria Corrias, Eddi Di Marco, Carola Bonaretti, Margherita Squillario, Loredana Amoroso, Massimo Conte, Mirco Ponzoni, Roberto Biassoni

“…By applying several algorithms to 16S sequencing, we found that the fecal microbiomes of infants with NB at onset were abundant in <i>Pseudomonadota</i>, including different descendants of <i>Gammaproteobacteria.…”
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Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm by Chiaki Kaga, Mina Okochi, Yasuyuki Tomita, Ryuji Kato, Hiroyuki Honda

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Identification of sepsis biomarkers through glutamine metabolism-mediated immune regulation: a comprehensive analysis employing mendelian randomization, multi-omics integration, an... by Zhuang’e Shi, Fuping Wang, Lishun Yang, Lishun Yang, Couwen Li, Bing Gong, Ruanxian Dai, Guobing Chen

“…The predictive models were constructed using the CatBoost, XGBoost, and NGBoost algorithms based on the data from GSE236713 and GSE28750. …”
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Leveraging Finite-Precision Errors in Chaotic Systems for Enhanced Image Encryption by B. M. El-Den, Saad Aldosary, Haitham Khaled, Tarek M. Hassan, Walid Raslan

“…The algorithm generates a keystream based on lower bound error and employs standard MATLAB routines to describe its main steps, including initialization and image factor addition, Chua&#x2019;s circuit simulation, error sequence generation, normalization, reshaping of the normalized sequence, and the encryption process. …”
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Cloning and Spatiotemporal Expression Analysis of <i>IGF1R</i> Gene cDNA in <i>Alopex lagopus</i> (Arctic Fox) by Wei Xu, Hualin Fu, Xiangyu Meng, Yiwen Sun, Fangyong Ning, Zhiheng Du

“…Phylogenetic analysis using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) algorithm revealed a 99% sequence homology in the <i>IGF1R</i> gene between the Arctic fox and canine, confirmed their closest evolutionary relationship. …”
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Noise injection into Freeman chain codes by Luka Lukač, Andrej Nerat, Damjan Strnad, Ivana Kolingerová, Borut Žalik

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SNUH methylation classifier for CNS tumors by Kwanghoon Lee, Jaemin Jeon, Jin Woo Park, Suwan Yu, Jae-Kyung Won, Kwangsoo Kim, Chul-Kee Park, Sung-Hye Park

“…Compared to two published CNS tumor methylation classification models (DKFZ-MC: Deutsches Krebsforschungszentrum Methylation Classifier v11b4: RandomForest, 767-MC: Multi-Layer Perceptron), our SNUH-MC showed improved performance in F1-score. …”
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Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning by Li-Wei Zhang, Ji Yang, Hua-Wei Jiang, Hua-Wei Jiang, Xiu-Qiang Yang, Ya-Nan Chen, Wei-Dang Ying, Ying-Liang Deng, Min-hui Zhang, Hai Liu, Hong-Lei Zhang

“…Additionally, we utilized weighted correlation network analysis (WGCNA) to select module genes and applied Random Forest (RF) and Support Vector Machine (SVM) algorithms to identify pivotal feature genes influencing pterygium progression. …”
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