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  1. 12121

    Bistability and oscillations in the Huang-Ferrell model of MAPK signaling. by Liang Qiao, Robert B Nachbar, Ioannis G Kevrekidis, Stanislav Y Shvartsman

    Published 2007-09-01
    “…As a result, these models try to predict the dynamics of systems with tens or even hundreds of free parameters. …”
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    Article
  2. 12122

    Pharmacotherapeutic Options for Ophthalmic Conjunctivitis by A. D. van Eyk

    Published 2019-07-01
    “…There is however a challenge in distinguishing between the various types of conjunctivitis due to the similarity in the symptoms and due to a lack of tests and prediction algorithms, thus antibiotic therapy is often incorrectly initiated. …”
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    Article
  3. 12123

    Integrative multi-omics module network inference with Lemon-Tree. by Eric Bonnet, Laurence Calzone, Tom Michoel

    Published 2015-02-01
    “…Novel candidate driver genes predicted by Lemon-Tree were validated using tumor pathway and survival analyses. …”
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    Article
  4. 12124

    Target Tracking Using a Time-Varying Autoregressive Dynamic Model by Ralph J. Mcdougall, Simon J. Godsill

    Published 2025-01-01
    “…Target tracking algorithms commonly use structured dynamic models which require prior training of fixed model parameters. …”
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    Article
  5. 12125

    Behavioral discrimination and time-series phenotyping of birdsong performance. by Avishek Paul, Helen McLendon, Veronica Rally, Jon T Sakata, Sarah C Woolley

    Published 2021-03-01
    “…Moreover, the algorithm accurately predicted female performance on individual motif exemplars. …”
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    Article
  6. 12126

    Organization of physical interactomes as uncovered by network schemas. by Eric Banks, Elena Nabieva, Bernard Chazelle, Mona Singh

    Published 2008-10-01
    “…Network schemas specify descriptions of proteins and the topology of interactions among them. We develop algorithms for systematically uncovering recurring, over-represented schemas in physical interaction networks. …”
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    Article
  7. 12127

    A conditional opposition-based particle swarm optimisation for feature selection by Jingwei Too, Ali Safaa Sadiq, Seyed Mohammad Mirjalili

    Published 2022-12-01
    “…Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying classification algorithms. As a popular metaheuristic algorithm, particle swarm optimisation has successfully applied to various feature selection approaches. …”
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    Article
  8. 12128

    Propensity to childhood anxiety and depression due to exposure to adversity: A multidimensional construct. by Angélica C G Santos, Ariane Silva, Matheus Libório, Cristiane Neri Nobre, Hasheem Mannan, Marcos Flávio S V D'Angelo

    Published 2025-01-01
    “…The literature does not present a consensus on the composition of indicators that make predictions, prognostic algorithms, or management strategies in mental health promotion and prevention. …”
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    Article
  9. 12129

    A Review on Machine Learning Models in Injection Molding Machines by Senthil Kumaran Selvaraj, Aditya Raj, R. Rishikesh Mahadevan, Utkarsh Chadha, Velmurugan Paramasivam

    Published 2022-01-01
    “…Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. …”
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    Article
  10. 12130

    TransUNet Image 3D Reconstruction with Hyperparameter Optimization by Xiaofang Wang, Zhihao Luo, Mingrui Gou, Kerui Mao, Liang Zhou

    Published 2025-01-01
    “…The method employs Restricted Boltzmann Machine (RBM) for depth prediction and an optimized ant colony algorithm for network parameter optimization. …”
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    Article
  11. 12131

    Consumer Happiness in the Purchase of Electric Vehicles: a Fuzzy Logic Model by Fernando Lámbarry-Vilchis, Aboud Barsekh Onji, Leticia Refugio Chavarría López, Paola Judith Maldonado Colín

    Published 2025-01-01
    “…This research was conducted using a fuzzy Delphi method survey targeting a specific consumer group and two fuzzy inference systems: a multi-input single-output FIS model and an FIS Tree employing a hierarchical fuzzy inference structure, which leverages the survey's training data to optimize the models using different machine learning algorithms. The FIS tree model demonstrated superior efficacy in predicting the consumer satisfaction index, achieving an average forecast error of 0.65%. …”
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    Article
  12. 12132

    Vaginal dysbiosis as a factor contributing to the development of recurrent lower urinary tract infections by I. V. Kosova, V. A. Barsegian, L. A. Sinyakova, I. V. Lukianov, D. N. Kolbasov

    Published 2023-04-01
    “…The examination was performed according to the algorithm developed in the Department of Urology and Surgical Andrology, Russian Medical Academy of Continuous Professional Education. …”
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    Article
  13. 12133

    Association between the development of sepsis and the triglyceride-glucose index in acute pancreatitis patients: a retrospective investigation utilizing the MIMIC-IV database by Lin Xu, Xuejing Li, Na Zhang, Chunmei Guo, Pan Wang, Min Gao, Yanhui Zhang, Lixin Zhao

    Published 2025-02-01
    “…Utilizing the formula ln[(triglycerides mg/dl) × (glucose mg/dl)/2], the TyG index was calculated. The Boruta algorithm and Xgboost model were used for feature selection in order to pinpoint the important variables affecting results. …”
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    Article
  14. 12134

    Validation of ICD‐10 Consensus Code Set for Cirrhosis Detection Using Electronic Health Records in an Asian Population by Jason Pik‐Eu Chang, Hong‐Yi Lin, Pooi‐Ling Loi, Jeanette Pei‐Xuan Ng, Marianne De Roza, Rahul Kumar, Hiang‐Keat Tan, Chanda Kendra Ho, Wei‐Quan Teo, Amber Hwa Hwa Chung, Prema Raj

    Published 2025-05-01
    “…We evaluated the consensus code set's sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in identifying cirrhosis cases. …”
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    Article
  15. 12135

    A prognostic glycolysis-related gene signature in osteosarcoma: implications for metabolic programming, immune microenvironment, and drug response by Naiqiang Zhu, Jingyi Hou, Yu Zhang, Ning Yang, KaiKai Ding, Chengbing Chang, Yanqi Liu, Haipeng Gu, Bin Chen, Xu Wei, Liguo Zhu

    Published 2025-04-01
    “…The prognostic efficacy of this risk signature was assessed via Kaplan–Meier curve analysis and Cox regression, evaluating its independence as a prognostic indicator. Additionally, the predictive potential of the risk model for drug response was evaluated using the “OncoPredict” package. …”
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    Article
  16. 12136
  17. 12137

    Generating Deeply-Engineered Technical Features for Basketball Video Understanding by Shaohua Fang, Guifeng Wang, Yongbin Li, Yue Yu, Jun Li

    Published 2025-01-01
    “…Our main contributions include: 1) an LSTM-based deep learning architecture for player action recognition and prediction; 2) a clustering-based algorithm for basketball court and line detection; and 3) a keyframe selection technique for basketball videos based on spatial-temporal scoring. …”
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    Article
  18. 12138

    Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model by Lizhi Pan, Diyi Liu, Ruyi Wang, Jinhua Li

    Published 2025-01-01
    “…For offline experiment, another two MMs were established and taken as the control groups for validation of the proposed DC-NMF-MM, including the MM totally taking the NMF algorithm (T-NMF-MM) and that partly taking the NMF algorithm (P-NMF-MM) for predicting the wrist pronation/supination only. …”
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  19. 12139

    Diagnostic Value of F-FDG PET/CT Radiomics in Lymphoma: A Systematic Review and Meta-Analysis by Chaoying Liu MD, Jun Zhao PhD, Heng Zhang PhD, Xinye Ni PhD

    Published 2025-05-01
    “…Six meta-regressions were conducted on study performance, considering sample size, image modality, region of interest (ROI) selection, ROI segmentation, radiomics mode, and algorithms. Results In total, 20 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement were included for this systematic review and meta-analysis. …”
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  20. 12140

    Modelling of River-Groundwater Interactions under Rainfall Events Based on a Modified Tank Model by Wen Nie, Yong-chang Liang, Lin Chen, Wei Shao

    Published 2017-01-01
    “…Parameter training of our tank model includes two algorithms: (i) the nonincremental learning algorithm-based model can predict the pore water pressure (PWP) in a slope and river under a 65 mm/hr rainfall event (coarse material) with Nash–Sutcliffe efficiency (NSE) = 0.427 and −0.909 and (ii) the incremental learning algorithm-based model can predict the PWP in a slope and river with NSE = 0.994 and 0.995. …”
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    Article