Identification of potential diagnostic markers and molecular mechanisms of asthma and ulcerative colitis based on bioinformatics and machine learning by Chenxuyu Zhang, Chenxuyu Zhang, Zheng Luo, Liang Ji

“…Machine learning identified hub genes, validated through external datasets using ROC curves, nomograms, and boxplots. …”
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Comparing Health Care Financing in Indonesia and Thailand from 1995-2010: What Lessons Could Be Learned? by Darius Erlangga, Lizheng Shi

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A lightweight and optimized deep learning model for detecting banana bunches and stalks in autonomous harvesting vehicles by Duc Tai Nguyen, Phuoc Bao Long Do, Doan Dang Khoa Nguyen, Wei-Chih Lin

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An Ensemble Fuzzy-Based Deep Learning Framework for Automatic Detection of Children with ADHD From EEG Signal by Jalil Manafian, Mehdi Fazli, Onur Ilhan, Subhiya Zeynalli, Sukaina Tuama Ghafel

“…This paper presents a novel technique utilizing deep learning to construct images from EEG signals. The approach involves creating two images from EEGs, utilizing both functional connectivity and spectral attributes. …”
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3D Visualization System of Breast Magnetic Resonance Images Based on Deep Learning and Volume Rendering by Rong Chen, Stephen Karungaru, Kenji Terada, Changyan Xu

“…Finally, an interactive visualization system is developed based on a client/server (C/S) three-tier architecture, enabling functions such as data loading, slice visualization, lesion segmentation, 3D rendering, and interface interaction. …”
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Investigating the Relationship between Ulcerative Colitis and Circadian Rhythm Genes Using Bioinformatics Analysis and Machine Learning by Wang J, You M, Li J, Wang X, Liu Y, Chen S, Wang P

“…Differentially expressed genes (DEGs) were identified using the “limma” package, and functional enrichment analysis was performed. Machine learning methods (LASSO, SVM, RF) refined candidate biomarkers. …”
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Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis by Xudong Shen, Xudong Shen, Xudong Shen, Guoxiang Li, Guoxiang Li, Guoxiang Li, Junfeng Yao, Junfeng Yao, Junfeng Yao, Junping Yang, Xiaobo Ding, Xiaobo Ding, Xiaobo Ding, Zongyao Hao, Zongyao Hao, Zongyao Hao, Yan Chen, Yang Chen, Yang Chen, Yang Chen

“…Among 101 machine learning models, S100A4, ARPC1B, and CEBPD were identified as the most significant interacting genes linking diabetes and kidney stones. …”
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Advancements in deep learning for early diagnosis of Alzheimer’s disease using multimodal neuroimaging: challenges and future directions by Muhammad Liaquat Raza, Muhammad Liaquat Raza, Muhammad Liaquat Raza, Syed Tawassul Hassan, Subia Jamil, Noorulain Hyder, Kinza Batool, Sajidah Walji, Muhammad Khizar Abbas

“…Recent advancements in deep learning algorithms applied to multimodal brain imaging offer promising solutions for improving diagnostic accuracy and predicting disease progression.MethodThis narrative review synthesizes current literature on deep learning applications in Alzheimer’s disease diagnosis using multimodal neuroimaging. …”
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Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning by Tee-Tau Eric Nyam, Kuan-Chi Tu, Nai-Ching Chen, Che-Chuan Wang, Chung-Feng Liu, Ching-Lung Kuo, Jen-Chieh Liao

“…Background: Traumatic brain injury (TBI) research often focuses on mortality rates or functional recovery, yet the critical need for long-term care among patients dependent on institutional or Respiratory Care Ward (RCW) support remains underexplored. …”
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Dynamic frailty risk prediction in elderly hip replacement: a deep learning approach to personalized rehabilitation by Xujing Lv, Hongmei Li, Yue Li, Ruibing Zhuo, Yiting Yue, Ying Wang, Xiaoyun Zheng, Huanling Gao

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METHODOLOGICAL ASPECTS OF FORMATION OF SUBJECT AND COMMUNICATIVE COMPETENCIES OF FOREIGN STUDENTS WHEN LEARNING PHYSICS AT PREPARATORY DEPARTMENTS by Olexandr О. Kalenyk, Tetiana L. Tsaregradska, Inna V. Plyushchay

“…The leading methodological direction of foreign students’ training for higher education institutions is the introduction of a functional and semantic approach, one aspect of which is the formation of communicative-oriented competencies (subject-speech), which ensures the communication of students in the educational and professional field. …”
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The Impact of Digital Technology Use on Teaching Quality in University Physical Education: An Interpretable Machine Learning Approach by Liguo Zhang, Zetan Liu, Liangyu Zhao, Jiarui Gao

“…Moreover, perceived teacher support and academic self-efficacy not only served as independent mediators (95% CI = [0.146, 0.226], <i>p</i> < 0.001; 95% CI = [0.024, 0.063], <i>p</i> < 0.001) but also functioned as a chain mediating effect (95% CI = [0.036, 0.083], <i>p</i> < 0.001). …”
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Elucidating the role of LGALS3BP in coronary atherosclerosis: integrating bioinformatics and machine learning for advanced insights by Suzhen Zhao, Chuanxin Zhao, Linlin Wang, Baoe Cheng, Man Zheng, Xiuyun Wang

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AutoTarget: Disease-Associated druggable target identification via node representation learning in PPI networks by Hyunseung Kong, Inyoung Kim, Byoung-Tak Zhang

“…This study presents AutoTarget, a novel computational pipeline designed to identify disease-associated druggable targets by applying node representation learning to protein–protein interaction (PPI) networks. …”
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RL-Cervix.Net: A Hybrid Lightweight Model Integrating Reinforcement Learning for Cervical Cell Classification by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…<b>Background:</b> Reinforcement learning (RL) represents a significant advancement in artificial intelligence (AI), particularly for complex sequential decision-making challenges. …”
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EEG–fNIRS signal integration for motor imagery classification using deep learning and evidence theory by Mohammed E. Seno, Niladri Maiti, Maulik Patel, Mihirkumar M. Patel, Kalpesh B. Chaudhary, Ashish Pasaya, Babacar Toure

“…To address the limitations of traditional unimodal brain-computer interface BCI) technologies based on electroencephalography (EEG) such as low spatial resolution and high susceptibility to noise an increasing number of neuroscience-driven studies have begun to focus on BCI systems that fuse EEG signals with functional near-infrared spectroscopy (fNIRS) signals. …”
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Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis by Caroline L Alves, Loriz Francisco Sallum, Francisco Aparecido Rodrigues, Thaise G L de O Toutain, Patrícia Maria de Carvalho Aguiar, Michael Moeckel

“…Machine learning models were applied for both binary (PD vs HC) and multiclass (HC vs early vs advanced PD) classification. …”
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Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches by Haiyang Zhang, Shuo Wu, Lehan Yang, Chengjing Fan, Huifang Chen, Hui Wang, Tianyi Zhu, Yinwei Li, Jing Sun, Xuefei Song, Huifang Zhou, Terry J Smith, Xianqun Fan

“…This study aims to investigate the factors influencing visual functioning (QOL-VF) and physical appearance (QOL-AP) scores in Chinese TED patients using innovative data analysis methods. …”
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An innovative model based on machine learning and fuzzy logic for tracking lower limb exercises in stroke patients by Utpal Chandra Das, Ngoc Thien Le, Timporn Vitoonpong, Chalermdej Prapinpairoj, Kawee Anannub, Wasan Akarathanawat, Aurauma Chutinet, Nijasri Charnnarong Suwanwela, Pasu Kaewplung, Surachai Chaitusaney, Watit Benjapolakul

“…Abstract Rehabilitation after a stroke is vital for regaining functional abilities. However, a shortage of rehabilitation professionals leads to many patients with severe disabilities. …”
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Identification of potential biomarkers and mechanisms for keloid disorder based on comprehensive bioinformatics analysis and machine learning algorithms by Bowen Zheng, Jianxiong Qiao, Xiaoping Yu, Hanghang Zhou, Anqi Wang, Xuanfen Zhang

“…This study sought to identify biomarkers and potential therapeutic targets for KD through an integrative bioinformatics approach and machine learning analysis of RNA sequencing data. Methods RNA sequencing was performed on skin tissue samples from 13 patients with KD and 14 healthy controls. …”
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