Use of Machine Learning to Predict the Incidence of Type 2 Diabetes Among Relatively Healthy Adults: A 10-Year Longitudinal Study in Taiwan by Ying-Qiang Liu, Tzu-Wei Chang, Lung-Chun Lee, Chia-Yu Chen, Pi-Shan Hsu, Yu-Tse Tsan, Chao-Tung Yang, Wei-Min Chu

“…Although machine learning (ML) models and data mining approaches have been applied to diabetes research, there was no study utilizing ML models to predict diabetes incidence in Taiwan. …”
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Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization by Revathy Venkataramanan, Revathy Venkataramanan, Aalap Tripathy, Tarun Kumar, Sergey Serebryakov, Annmary Justine, Arpit Shah, Suparna Bhattacharya, Martin Foltin, Paolo Faraboschi, Kaushik Roy, Amit Sheth

“…It guides AI pipeline optimization, offers insights and recommendations for improving AI pipelines, and serves as a foundation for data mining and analysis of evolving AI workflows.…”
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Adverse drug events (ADEs) risk signal mining related to eculizumab based on the FARES database by Xi-Feng Wang, Lu-Ri Bao, Ta-La Hu, Rui-Feng Xu, Wu-Niri Gao, Jing-Yuan Wang, Jian-Rong Zhao, Zhen-Li Fu, Shu-Fang Wang, Yan Meng

“…The current study was conducted to assess real-world adverse events (AEs) associated with eculizumab through data mining of the FDA Adverse Event Reporting System (FAERS).MethodsDisproportionality analyses, including Reporting Ratio Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS) algorithms were used to quantify the signals of eculizumab-associated AEs.ResultsA total of 46,316 eculizumab-related ADEs reports were identified by analyzing 19,418,776 reports in the U.S. …”
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Therapeutic target genes and regulatory networks of gallic acid in cervical cancer by Zhixi You, Ye Lei, Yongkang Yang, Zhihui Zhou, Xu Chao, Keyi Ju, Songyi Wang, Yuanyuan Li, Yuanyuan Li

“…The upstream 13 miRNAs, 4 lncRNA, and 42 circRNAs were detected and used to construct a lncRNA/circRNA-miRNA-mRNA-pathway regulatory network.ConclusionThis study identified candidate genes and the regulatory networks underlying the therapeutic effects of GA on CC using GA data mining methods, thus establishing a theoretical basis for targeted therapy of CC.…”
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Network pharmacology-based screening of the active ingredients and mechanisms of Cymbaria daurica against diabetes mellitus by Ruyu Shi, Dongxue Chen, Mingyue Ji, Baochang Zhou, Ziyan Zhang, Chunhong Zhang, Minhui Li

“…In this study, the potential mechanism of C. daurica against type 2 diabetes mellitus (T2DM) was postulated via pharmacological network analysis. Based on data mining techniques involving topological parameters, gene ontology, and pathway enrichment, we established a compound-target, protein-protein interaction, and target-pathway network to identify central targets and pathways. …”
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THE SOLANUM TUBEROSUM KNOWLEDGE BASE: THE SECTION ON MOLECULAR-GENETIC REGULATION OF METABOLIC PATHWAYS by T. V. Ivanisenko, O. V. Saik, P. S. Demenkov, V. K. Khlestkin, E. K. Khlestkina, N A. Kolchanov, V. А. Ivanisenko

“…Effective access to knowledge distributed over such a multitude of non-formalized natural language textual sources requires the use of special computer-assisted intelligent methods of data mining (text-mining). However, in the literature, there is no data on the application of intellectual methods of automatic knowledge extraction from publications on agricultural crops, such as potato. …”
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The association between bone density of lumbar spines and different daily protein intake in different renal function by Chia-Lin Lee, Kun-Hui Chen, Wei‑Ju Liu, Ching-Hsien Chen, Shang-Feng Tsai

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Fournier Gangrene Associated with Sodium-Glucose Cotransporter-2 Inhibitors: A Pharmacovigilance Study with Data from the U.S. FDA Adverse Event Reporting System by Yang Hu, Ziyu Bai, Yan Tang, Rongji Liu, Bin Zhao, Jian Gong, Dan Mei

“…Disproportionality analysis and Bayesian analysis were applied for data mining among the suspected adverse event reports of FG associated with SGLT2 inhibitors recorded in the FAERS database during the period from January 2004 to September 2019. …”
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Dual Fine-Grained network with frequency Transformer for change detection on remote sensing images by Zhen Li, Zhenxin Zhang, Mengmeng Li, Liqiang Zhang, Xueli Peng, Rixing He, Leidong Shi

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Real‐world clinical diagnostics of heart failure patients with reduced or preserved ejection fraction by Jenni Huusko, Timo Purmonen, Iiro Toppila, Mariann Lassenius, Heikki Ukkonen

“…Among patients with an International Classification of Diseases, Tenth Revision (ICD‐10) code for HF, only 50% had ejection fraction (EF) data to be found by data mining from the electronic health records. Of these patients, 39% (n = 4042) had EF ≤ 40% [HF with reduced EF (HFrEF)] and 61% (n = 6347) had EF > 40%. …”
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A multidimensional assessment of adverse events associated with paliperidone palmitate: a real-world pharmacovigilance study using the FAERS and JADER databases by Siyu Lou, Zhiwei Cui, Yingyong Ou, Junyou Chen, Linmei Zhou, Ruizhen Zhao, Chengyu Zhu, Li Wang, Zhu Wu, Fan Zou

“…As such, this study aimed to comprehensively evaluate real-world adverse drug events (ADEs) linked to paliperidone palmitate by employing data mining techniques on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database and the Japanese Adverse Drug Event Report (JADER) database. …”
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Safety profiles of IDH inhibitors: a pharmacovigilance analysis of the FDA Adverse Event Reporting System (FAERS) database by Ximu Sun, Han Zhou, Yanming Li, Yanhui Luo, Qixiang Guo, Yixin Sun, Chenguang Jia, Bin Wang, Maoquan Qin, Peng Guo

“…Disproportionality analyses including the reporting odds ratio and the Bayesian confidence propagation neural network were performed in data mining to assess IDH inhibitor-relatedAEs. Differentiation syndrome was the AE of special interest. …”
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Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds by Mengdie Fan, Chenhui Sang, Hua Li, Yue Wei, Bin Zhang, Yang Xing, Jing Zhang, Jie Yin, Wei An, Bing Shao

“…This study provides a scientific basis for high throughput and rapid prediction of unknown pollutants, data mining, nontargeted screening, etc.…”
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Pemanfaatan Data PDDIKTI sebagai Pendukung Keputusan Manajemen Perguruan Tinggi by Ngatmari Ngatmari, Muhammad Bisri Musthafa, Cahya Rahmad, Rosa Andrie Asmara, Faisal Rahutomo

“…The purpose of PDDIKTI data mining is to conduct early detection of students, so that lecturers can provide input when the students have been classified as students who graduate not on time and predict the number of students who will enter the tertiary institutions in one of the X study programs, so that management both the level of study program and university can take steps that are considered important to increase the number of students. …”
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Implementasi Algoritma Random Forest Untuk Menentukan Penerima Bantuan Raskin by Ilham Kurniawan, Duwi Cahya Putri Buani, Abdussomad Abdussomad, Widya Apriliah, Rizal Amegia Saputra

“…Classification is a data mining method that determines categories in data groups to support more accurate predictions and analysis. …”
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Review and Prospect of Research on Urban Walking Safety and Environmental Influencing Factors Thereof by Lin LI, Yu YE, Yong CHEN

“…Especially in the field of data mining regarding urban form and walkers’ psychological perception, new technologies are helpful for in-depth analysis of the influence of built environment factors on walking safety. …”
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Ultrasound May Suppress Tumor Growth, Inhibit Inflammation, and Establish Tolerogenesis by Remodeling Innatome via Pathways of ROS, Immune Checkpoints, Cytokines, and Trained Immun... by Qian Yang, Ruijing Zhang, Peng Tang, Yu Sun, Candice Johnson, Jason Saredy, Susu Wu, Jiwei Wang, Yifan Lu, Fatma Saaoud, Ying Shao, Charles Drummer, Keman Xu, Daohai Yu, Rongshan Li, Shuping Ge, Xiaohua Jiang, Hong Wang, Xiaofeng Yang

“…We used microarray datasets from the NCBI GEO DataSet repository and conducted comprehensive data-mining analyses, where we examined the gene expression of 1376 innate immune regulators (innatome genes (IGs) in cells treated with LIUS. …”
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An integrative analysis reveals cancer risk associated with artificial sweeteners by Jumin Xie, Ying Zhu, Zixuan Yang, Zhang Yu, Mingzhi Yang, Qingzhi Wang

“…Conclusion This study integrated data mining, machine learning, network toxicology, molecular docking, molecular dynamics simulations, and clinical sample analysis to demonstrate that AS increases the risk of kidney cancer, low-grade glioma, breast cancer, and prostate cancer through multiple targets and signaling pathways. …”
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Therapeutic depletion of CCR8+ tumor-infiltrating regulatory T cells elicits antitumor immunity and synergizes with anti-PD-1 therapy by James Dooley, Bart Neyns, Elizabeth Allen, Frank Tacke, Louis Boon, Damya Laoui, Julia Katharina Schwarze, Helena Van Damme, Bruno Dombrecht, Máté Kiss, Heleen Roose, Eva Van Overmeire, Daliya Kancheva, Liesbet Martens, Aleksandar Murgaski, Pauline Madeleine Rachel Bardet, Gillian Blancke, Maude Jans, Evangelia Bolli, Maria Solange Martins, Yvon Elkrim, Kiavash Movahedi, Niels Vandamme, Sebahat Ocak, Isabelle Scheyltjens, Lars Vereecke, Frank Aboubakar Nana, Pascal Merchiers, Jo Agnes Van Ginderachter

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Adverse events in the nervous system associated with blinatumomab: a real-world study by Wen Gao, Jingwei Yu, Yifei Sun, Zheng Song, Xia Liu, Xue Han, Lanfan Li, Lihua Qiu, Shiyong Zhou, Zhengzi Qian, Xianhuo Wang, Huilai Zhang

“…The reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence interval progressive neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) algorithms were utilized for data mining. Results A total of 5,962 blinatumomab-related cases were analyzed. …”
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