Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation. by Katherine I Morley, Joshua Wallace, Spiros C Denaxas, Ross J Hunter, Riyaz S Patel, Pablo Perel, Anoop D Shah, Adam D Timmis, Richard J Schilling, Harry Hemingway

“…<h4>Results</h4>The phenotype algorithm incorporated 286 codes: 201 Read, 63 BNF, 18 ICD-10, and four OPCS-4. …”
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Android malware detection based on APK signature information feedback by Xin-yu LIU, Jian WENG, Yue ZHANG, Bing-wen FENG, Jia-si WENG

“…A new malware detection method based on APK signature of information feedback (SigFeedback) was proposed.Based on SVM classification algorithm,the method of eigenvalue extraction adoped heuristic rule learning to sig APK information verify screening,and it also implemented the heuristic feedback,from which achieved the purpose of more accurate detection of malicious software.SigFeedback detection algorithm enjoyed the advantage of the high detection rate and low false positive rate.Finally the experiment show that the SigFeedback algorithm has high efficiency,making the rate of false positive from 13% down to 3%.…”
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Android malware detection based on APK signature information feedback by Xin-yu LIU, Jian WENG, Yue ZHANG, Bing-wen FENG, Jia-si WENG

“…A new malware detection method based on APK signature of information feedback (SigFeedback) was proposed.Based on SVM classification algorithm,the method of eigenvalue extraction adoped heuristic rule learning to sig APK information verify screening,and it also implemented the heuristic feedback,from which achieved the purpose of more accurate detection of malicious software.SigFeedback detection algorithm enjoyed the advantage of the high detection rate and low false positive rate.Finally the experiment show that the SigFeedback algorithm has high efficiency,making the rate of false positive from 13% down to 3%.…”
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Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma by Zhengyu Wei, Guoli Wang, Yanghao Hu, Chongchang Zhou, Yuna Zhang, Yi Shen, Yaowen Wang

“…Results Through the application of three machine learning algorithms, five key genes (LTF, IDH1, ITGAV, CCL2, and LGALS3BP) were identified for the construction of a diagnostic model. …”
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Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors by Ran Zhang, Guo Chen, Shasha Gao, Lu Chen, Yongchao Cheng, Xiuquan Gu, Yue Wang

“…The collected data was subsequently utilized to develop a correlation model linking the multi-physical parameters to gas sensitive performance using intelligent algorithms. …”
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Evaluating the role of insulin resistance in chronic intestinal health issues: NHANES study findings by Dongyao Zhao, Meihua Zhao, Bing Gao, He Lu

“…Key variables were selected via the Boruta algorithm and incorporated into weighted multivariate logistic regression models. …”
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Emergency scheduling strategy of integrated electricity-gas energy system considering wind-power fluctuation in typhoon disaster by JIN Haixiang, BIAN Xiaoyan, HUANG Ruanming, ZHOU Qibin, XU Ling

“…The adaptive-alternating direction method of multipliers (AT-ADMM) algorithm is adopted to solve the model. An example is given to verify the effectiveness of the proposed method.…”
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A hybrid super learner ensemble for phishing detection on mobile devices by Routhu Srinivasa Rao, Cheemaladinne Kondaiah, Alwyn Roshan Pais, Bumshik Lee

“…Abstract In today’s digital age, the rapid increase in online users and massive network traffic has made ensuring security more challenging. Among the various cyber threats, phishing remains one of the most significant. …”
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The CD163 + tissue-infiltrating macrophages regulate ferroptosis in thyroid-associated ophthalmopathy orbital fibroblasts via the TGF-β/Smad2/3 signaling pathway by Xuemei Li, Siyi Wang, Hanwen Cao, Simin Xu, Chao Xiong, Jinhai Yu, Yunxiu Chen, Zhangjun Ren, Min Li, Ying Hu, Puying Gan, Qihua Xu, Yaohua Wang, Hongfei Liao

“…Finally, potential clinical drugs targeting CD163 + macrophages with high ferroptosis activity in TAO were predicted using the Random Walk with Restart (RWR) algorithm combined with the DGIdb database. Results We first utilized TAO-related datasets from the GEO database, combined with the FerrDb ferroptosis database, to identify changes in iron metabolism genes during TAO progression through differential expression analysis, screening 7 key ferroptosis-related proteins. …”
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Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015 by Dandan Tang, Man Zhang, Jiabo Xu, Xueliang Zhang, Fang Yang, Huling Li, Li Feng, Kai Wang, Yujian Zheng

“…The goal of this study was to use four data mining algorithms to establish the identification model of HIV infection and compare their predictive performance. …”
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Thirty-day mortality risk prediction for geriatric patients undergoing non-cardiac surgery in the surgical intensive care unit by Mengke Ma, Jiatong Liu, Caiyun Li, Yingxue Chen, Huishu Jia, Aijie Hou, Hongzeng Xu

“…The least absolute shrinkage selection operator (LASSO) regularization algorithm and the extreme gradient boosting (XGBoost) for feature importance evaluation were used to screen important predictors. …”
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Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for thyroid cancer by Peng Zhang, Meizhong Qin, Fen Li, Kunpeng Hu, He Huang, Cuicui Li

“…Abstract Background Thyroid cancer (THCA) exhibits high molecular heterogeneity, posing challenges for precise prognosis and personalized therapy. Most existing models rely on single-omics data and limited algorithms, reducing robustness and clinical value. …”
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A Framework for Segmentation and Classification of Cervical Cells Under Long-tailed Distribution by YANG Xiao na, LI Chao wei, SHAO Hui li, HE Yong jun

“…This framework first performs cell nucleus segmentation, uses U-Net as the base model for layer reduction, adds AG module, and uses ACBlock module instead of traditional standard convolutional blocks; then uses ResNeSt for coarse classification of segmented data, fuses manual features extracted based on physicians ′ experience and machine features extracted by ResNeSt network for fine classification , and uses active learning iteratively to expand the cervical cell categories and fuse the ACBlock module in the BBN model to process the long-tail data; finally, the diagnostic indexes of abnormal cells are refined and abnormal cells are screened according to the TBS diagnostic criteria and the physician ′s diagnostic experience. …”
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Artificial Intelligence in Pediatric Blood Transfusion during Anesthesia: A Scoping Review by Parisa Akbarpour, Parisa Moradimajd, Azam Saei, Maryam Aligholizadeh, Siavash Sangi

“…Relevant keywords, including artificial intelligence, machine learning, predictive model, neural network, predictive algorithm, blood transfusion, children, pediatric, neonates, anesthesia, surgery, and operation, were extracted from the Medical Subject Headings (MeSH). …”
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Bioinformatics analysis of potential targets influencing the prognosis of OSCC by ZOU Xian, SONG Tao

“…The Timer website was used to analyze the relationship between hub genes and immune cell infiltration and immune checkpoints. Based on Lasso-Cox algorithm, a prognostic risk model of related genes was constructed. …”
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Predicting radiation pneumonitis in lung cancer using machine learning and multimodal features: a systematic review and meta-analysis of diagnostic accuracy by Zhi Chen, GuangMing Yi, XinYan Li, Bo Yi, XiaoHui Bao, Yin Zhang, XiaoYue Zhang, ZhenZhou Yang, Zhengjun Guo

“…By selecting multiple machine learning algorithm frameworks and competing for the best combination model based on research goals, the reliability and accuracy of the radiation pneumonitis prediction model can be greatly improved. …”
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Oxidative stress gene expression in ulcerative colitis: implications for colon cancer biomarker discovery by Ting Yan, Ting Su, Miaomiao Zhu, Qiyuan Qing, Binjie Huang, Jun Liu, Tenghui Ma

“…Subsequently, we performed Gene Ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) analyses, followed by immune infiltration analysis using the single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT algorithms. By constructing a multivariate Cox prognostic model using Kaplan–Meier curves and least absolute shrinkage and selection operator (LASSO) regression analysis, we assessed the model’s prognostic capability. …”
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Multimodal ultrasound radiomics containing microflow images for the prediction of central lymph node metastasis in papillary thyroid carcinoma by Jiangyuan Ben, Jiangyuan Ben, Qiying Yv, Pengfei Zhu, Junhao Ren, Pu Zhou, Guifang Chen, Ying He, Ying He

“…The same methods were applied to screen clinical features. Nine ML algorithms were used to construct clinical models, radiomics models and fusion models. …”
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Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images by Mingzhi Zhang, Tsz Kin Ng, Yi Zheng, Guihua Zhang, Jian-Wei Lin, Ji Wang, Jie Ji, Peiwen Xie, Yongqun Xiong, Hanfu Wu, Cui Liu, Huishan Zhu, Jinqu Huang, Leixian Lin

“…The deep learning (DL) performance was compared with the diabetic retinopathy experts.Setting Data were collected from Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Chaozhou People’s Hospital and The Second Affiliated Hospital of Shantou University Medical College from January 2010 to December 2023.Participants 7790 volumes of 7146 eyes from 4254 patients were annotated, of which 6281 images were used as the development set and 1509 images were used as the external validation set, split based on the centres.Main outcomes Accuracy, F1-score, sensitivity, specificity, area under receiver operating characteristic curve (AUROC) and Cohen’s kappa were calculated to evaluate the performance of the DL algorithm.Results In classifying DME with non-DME, our model achieved an AUROCs of 0.990 (95% CI 0.983 to 0.996) and 0.916 (95% CI 0.902 to 0.930) for hold-out testing dataset and external validation dataset, respectively. …”
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Design and optimization of planetary gear train pendulum type sugarcane seeding mechanism based on spatial offset trajectory by Jiaodi Liu, Chaoyuan Luo, Qingli Chen, Jianhao Chen, Jianlong Chen, Yihao Xing

“…Based on the speed requirements of the sugarcane seeds at the critical motion points, a forward kinematics model of this seeding mechanism is established. A multi-objective genetic algorithm combined with the entropy-weight TOPSIS method is used to optimize and screen the installation dimensions of the components of the mechanism so as to keep the motion of the sugarcane seeds stable at the critical positions. …”
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