基于IMU信号的人工智能上肢多关节运动状态识别系统构建——卒中后人工智能运动功能评估与检测系统建设前导研究 Construction of an Artificial Intelligence Upper Limb Multi-Joint Motion State Recognition System Based on IMU Signals—A Pre... by 程相鑫1，张烁1，杜松骏1，刘子阳1，周宏宇2，3，贾伟丽2，3，李子孝2，3，刘涛1 (CHENG Xiangxin1, ZHANG Shuo1, DU Songjun1, LIU Ziyang1, ZHOU Hongyu2,3, JIA Weili2,3, LI Zixiao2,3, LIU Tao1 )

“…., independently training single-joint classifiers and then merging the outputs) was constructed. At the algorithm level, traditional machine learning methods (time-frequency domain features+random forest) were compared with deep learning algorithms (long short-term memory-based end-to-end learning). …”
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Deep-Learning-Based Computer-Aided Grading of Cervical Spinal Stenosis from MR Images: Accuracy and Clinical Alignment by Zhiling Wang, Xinquan Chen, Bin Liu, Jinjin Hai, Kai Qiao, Zhen Yuan, Lianjun Yang, Bin Yan, Zhihai Su, Hai Lu

“…<b>Objective:</b> This study aims to apply different deep learning convolutional neural network algorithms to assess the grading of cervical spinal stenosis and to evaluate their consistency with clinician grading results as well as clinical manifestations of patients. …”
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A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants by Cong Lai, Zhensheng Hu, Zhuohang Li, Zhikai Wu, Kuiqing Li, Lin Li, Hongze Liu, Juanyi Shi, Yi Zhou, Kewei Xu, Cheng Liu

“…Six machine learning algorithms were employed to develop a screening model for PCa using the training dataset. …”
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Diagnosis and activity prediction of SLE based on serum Raman spectroscopy combined with a two-branch Bayesian network by Qianxi Xu, Qianxi Xu, Qianxi Xu, Xue Wu, Xue Wu, Xue Wu, Xinya Chen, Ziyang Zhang, Jinrun Wang, Jinrun Wang, Zhengfang Li, Zhengfang Li, Zhengfang Li, Xiaomei Chen, Xiaomei Chen, Xiaomei Chen, Xin Lei, Xin Lei, Zhuoyu Li, Zhuoyu Li, Zhuoyu Li, Mengsi Ma, Mengsi Ma, Mengsi Ma, Chen Chen, Lijun Wu, Lijun Wu

“…Additionally, the model’s efficacy in classifying SLE disease activity was assessed.ConclusionThis study demonstrates the feasibility of Raman spectroscopy combined with deep learning algorithms to differentiate between SLE and non-SLE. …”
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Predicting Index Trend Using Hybrid Neural Networks with a Focus on Multi-Scale Temporal Feature Extraction in the Tehran Stock Exchange by Mohammad Osoolian, Ali Nikmaram, Mahdi Karimi

“…A wide array of predictive modeling techniques have been meticulously investigated, spanning from conventional statistical methodologies to more sophisticated machine learning algorithms. The primary focus of this research endeavor revolves around the predictive analysis of the Tehran Stock Exchange (TSE) Composite Index, wherein a novel hybrid neural network framework is employed. …”
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Construction and Validation of a Hospital Mortality Risk Model for Advanced Elderly Patients with Heart Failure Based on Machine Learning by Shang S, Wei M, Lv H, Liang X, Lu Y, Tang B

“…Subsequently, seven different machine learning models were constructed and their prediction performances were evaluated. …”
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Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech by Julianna Olah, Win Lee Edwin Wong, Atta-ul Raheem Rana Chaudhry, Omar Mena, Sunny X. Tang

“…Our study aimed to (1) identify an optimal assessment approach for the online and remote collection of speech, in the context of assessing the psychosis spectrum and evaluate whether a fully automated, speech-based machine learning (ML) pipeline can discriminate among different conditions on the schizophrenia-bipolar spectrum (SSD-BD-SPE), help-seeking comparison subjects (MDD), and healthy controls (HC) at varying layers of analysis and diagnostic complexity. …”
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Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran by Azadeh Ahmadi Kousha, Faegh Ahmadi, Mohammad Hossein Ranjbar, Hamidreza Kordlouie

“…Naive Bayes, Meta, Attribute Selected Classifier, and j48 algorithms were implemented and WEKA software was used to classify criteria and create patterns. …”
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Long-term prognosis of 47 pediatric patients with Blau syndrome in China by Xinwei Shi, Jianghong Deng, Junmei Zhang, Xiaozhen Zhao, Yinan Zhao, Li Li, Fengqiao Gao, Weiying Kuang, Jiang Wang, Xiaohua Tan, Chao Li, Shipeng Li, Caifeng Li

“…A Bayesian network was constructed to integrate prediction algorithms of genetic mutations and clinical manifestations, exploring the complex relationship between genotype and phenotype through R (Version 4.4.1, R Core Development Team). …”
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Human responses to the DNA prime/chimpanzee adenovirus (ChAd63) boost vaccine identify CSP, AMA1 and TRAP MHC Class I-restricted epitopes. by Harini Ganeshan, Jun Huang, Maria Belmonte, Arnel Belmonte, Sandra Inoue, Rachel Velasco, Santina Maiolatesi, Keith Limbach, Noelle Patterson, Marvin J Sklar, Lorraine Soisson, Judith E Epstein, Kimberly A Edgel, Bjoern Peters, Michael R Hollingdale, Eileen Villasante, Christopher A Duplessis, Martha Sedegah

“…Individual antigen-specific 15mers in the subpools with strong responses were then deconvoluted, evaluated for activities, and MHC Class I-restricted epitopes within the active 15mers were predicted using NetMHCpan algorithms. …”
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Project quality, regulation quality by Elena Mussinelli

“…Instead, deductive design approaches seem to prevail today, due to the growing availability of algorithmic procedures that do not merely support the design process, but develop it in an almost automated manner through conditioning and prevailing indicators and parameters. …”
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HARMFUL CONDITIONS ON THE DENTAL STATUS OF POULTRY FARM WORKERS by T. Pupin, O. Kardashevska

“…Harmful work conditions influence onto formation of different pathological processes throughout the body. …”
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Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study by Huiyu Yao, Zixin Cao, Liangfu Huang, Haojie Pan, Xiaomin Xu, Fucai Sun, Xi Ding, Wan Wu

“…Additionally, it evaluated a Random Forest machine learning model for classifying various oral mucosal diseases based on peripheral blood biomarkers. …”
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Resveratrol contributes to NK cell-mediated breast cancer cytotoxicity by upregulating ULBP2 through miR-17-5p downmodulation and activation of MINK1/JNK/c-Jun signaling by Bisha Ding, Jie Li, Jia-Lin Yan, Chun-Yan Jiang, Ling-Bo Qian, Jie Pan

“…The effects of RES on sensitivity of BC cells to NK cell cytotoxicity were evaluated in vitro and in vivo. The target gene of miR-17-5p were predicted with different algorithms from five databases and further confirmed with dual-luciferase reporter assay. …”
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A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…Based on this framework, we developed Model (i), which integrates SOC data with spatial-spectral resolution downscaling (SSD) image; Model (ii), which integrates SOC data, UAV image with spatial resolution downscaling (SD) image; and Model (iii), which integrates SOC data, UAV image with SSD image. We also evaluated the performance of various algorithms (e.g., Random Forest (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Multi-Layer Perceptron (MLP)) across these models. …”
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Multiple loci are associated with white blood cell phenotypes. by Michael A Nalls, David J Couper, Toshiko Tanaka, Frank J A van Rooij, Ming-Huei Chen, Albert V Smith, Daniela Toniolo, Neil A Zakai, Qiong Yang, Andreas Greinacher, Andrew R Wood, Melissa Garcia, Paolo Gasparini, Yongmei Liu, Thomas Lumley, Aaron R Folsom, Alex P Reiner, Christian Gieger, Vasiliki Lagou, Janine F Felix, Henry Völzke, Natalia A Gouskova, Alessandro Biffi, Angela Döring, Uwe Völker, Sean Chong, Kerri L Wiggins, Augusto Rendon, Abbas Dehghan, Matt Moore, Kent Taylor, James G Wilson, Guillaume Lettre, Albert Hofman, Joshua C Bis, Nicola Pirastu, Caroline S Fox, Christa Meisinger, Jennifer Sambrook, Sampath Arepalli, Matthias Nauck, Holger Prokisch, Jonathan Stephens, Nicole L Glazer, L Adrienne Cupples, Yukinori Okada, Atsushi Takahashi, Yoichiro Kamatani, Koichi Matsuda, Tatsuhiko Tsunoda, Toshihiro Tanaka, Michiaki Kubo, Yusuke Nakamura, Kazuhiko Yamamoto, Naoyuki Kamatani, Michael Stumvoll, Anke Tönjes, Inga Prokopenko, Thomas Illig, Kushang V Patel, Stephen F Garner, Brigitte Kuhnel, Massimo Mangino, Ben A Oostra, Swee Lay Thein, Josef Coresh, H-Erich Wichmann, Stephan Menzel, JingPing Lin, Giorgio Pistis, André G Uitterlinden, Tim D Spector, Alexander Teumer, Gudny Eiriksdottir, Vilmundur Gudnason, Stefania Bandinelli, Timothy M Frayling, Aravinda Chakravarti, Cornelia M van Duijn, David Melzer, Willem H Ouwehand, Daniel Levy, Eric Boerwinkle, Andrew B Singleton, Dena G Hernandez, Dan L Longo, Nicole Soranzo, Jacqueline C M Witteman, Bruce M Psaty, Luigi Ferrucci, Tamara B Harris, Christopher J O'Donnell, Santhi K Ganesh

“…We implemented gene-clustering algorithms to evaluate functional connectivity among implicated loci and showed functional relationships across cell types. …”
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Harnessing multi-omics and artificial intelligence: revolutionizing prognosis and treatment in hepatocellular carcinoma by Zhen Wang, Zhen Wang, Zhen Wang, Gangchen Zhou, Gangchen Zhou, Rongchuan Cao, Rongchuan Cao, Guolin Zhang, Guolin Zhang, Yongxu Zhang, Yongxu Zhang, Mingyue Xiao, Longbi Liu, Longbi Liu, Xuesong Zhang

“…To identify distinct molecular subtypes, a multi-omics data integration approach was employed, utilizing 10 distinct clustering algorithms. Survival analysis, immune infiltration profiling and drug sensitivity predictions were then used to evaluate the prognostic significance and therapeutic responses of these subtypes. …”
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Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy by Wuyuan Liao, Wuyuan Liao, Wuyuan Liao, Xinwei Zhou, Xinwei Zhou, Hansen Lin, Hansen Lin, Zihao Feng, Zihao Feng, Xinyan Chen, Yuhang Chen, Yuhang Chen, Minyu Chen, Minyu Chen, Mingjie Lin, Mingjie Lin, Gaosheng Yao, Gaosheng Yao, Jinwei Chen, Jinwei Chen, Haoqian Feng, Haoqian Feng, Yinghan Wang, Yinghan Wang, Zhiping Tan, Zhiping Tan, Youyan Tan, Jun Lu, Jun Lu, Pengju Li, Pengju Li, Jinhuan Wei, Jinhuan Wei, Li Luo, Li Luo, Liangmin Fu, Liangmin Fu, Liangmin Fu

“…We investigated its expression in tumor tissues and evaluated the impact of its knockdown on immunotherapeutic efficacy using in vitro and in vivo experiments.ResultsOur comprehensive analysis revealed that the predictive power of TMB varies significantly across different cancer types and is highly dependent on its interaction with the TME. …”
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Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer by Lin Ni, Lin Ni, He Li, Yanqi Cui, Wanqiu Xiong, Shuming Chen, Hancong Huang, Zhiwei Wang, Hu Zhao, Hu Zhao, Hu Zhao, Bing Wang, Bing Wang, Bing Wang

“…ObjectivesIn this study, we constructed a model based on circadian rhythm associated genes (CRRGs) to predict prognosis and immune infiltration in patients with breast cancer (BC).Materials and methodsBy using TCGA and CGDB databases, we conducted a comprehensive analysis of circadian rhythm gene expression and clinicopathological data. Three different machine learning algorithms were used to screen out the characteristic circadian genes associated with BC prognosis. …”
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What happens between first symptoms and first acute exacerbation of COPD – observational study of routine data and patient survey by Alex Bottle, Alex Adamson, Xiubin Zhang, Benedict Hayhoe, Jennifer K Quint

“…However, there is limited understanding of what prompts a diagnosis, how long this takes from symptom onset and the different approaches to clinical management by primary care professionals. …”
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