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Showing 161 - 180 results of 258 for search '(( binary data based optimization algorithm ) OR ( final based proteins optimization algorithm ))', query time: 1.31s Refine Results
  1. 161
    Table_1_v1_Prediction of acute kidney injury in patients with femoral neck fracture utilizing machine learning.docx

    Table_1_v1_Prediction of acute kidney injury in patients with femoral neck fracture utilizing machine learning.docx by Jun Liu (42548)

    Published 2022
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  2. 162
    DataSheet1_Poro-viscoelastic material parameter identification of brain tissue-mimicking hydrogels.pdf

    DataSheet1_Poro-viscoelastic material parameter identification of brain tissue-mimicking hydrogels.pdf by Manuel P. Kainz (15201970)

    Published 2023
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  3. 163
    Video_1_Haptic Glove Using Tendon-Driven Soft Robotic Mechanism.MP4

    Video_1_Haptic Glove Using Tendon-Driven Soft Robotic Mechanism.MP4 by Siyeon Baik (9498911)

    Published 2020
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  4. 164
    Video_1_Haptic Glove Using Tendon-Driven Soft Robotic Mechanism.MP4

    Video_1_Haptic Glove Using Tendon-Driven Soft Robotic Mechanism.MP4 by Siyeon Baik (9498911)

    Published 2020
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  5. 165
    An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach

    An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach by Sanaa Badr (20628838)

    Published 2025
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  6. 166
    Bayesian sequential design for sensitivity experiments with hybrid responses

    Bayesian sequential design for sensitivity experiments with hybrid responses by Yuxia Liu (1779592)

    Published 2023
    “…To deal with the problem of complex computation involved in searching for optimal designs, fast algorithms are presented using two strategies to approximate the optimal criterion, denoted as SI-optimal design and Bayesian D-optimal design, respectively. …”
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  7. 167
    Data_Sheet_1_Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin.ZIP

    Data_Sheet_1_Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin.ZIP by Francesco Delfino (7496873)

    Published 2019
    “…We finally compare this trajectory with that produced by the online tool MinActionPath, by minimizing the action integral using a harmonic network model, and with that obtained by the PROMPT morphing method, based on an optimal mass transportation-type approach including physical constraints. …”
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  8. 168
    Data_Sheet_2_Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin.ZIP

    Data_Sheet_2_Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin.ZIP by Francesco Delfino (7496873)

    Published 2019
    “…We finally compare this trajectory with that produced by the online tool MinActionPath, by minimizing the action integral using a harmonic network model, and with that obtained by the PROMPT morphing method, based on an optimal mass transportation-type approach including physical constraints. …”
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  9. 169
    Table_2_Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers.xlsx

    Table_2_Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers.xlsx by Mark Uhlik (628328)

    Published 2023
    “…</p>Methods<p>The Panel algorithm is an artificial neural network (ANN) trained with an input signature of 124 genes that was optimized across various solid tumors. …”
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  10. 170
    Table1_Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.XLSX

    Table1_Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.XLSX by Qiang-Sheng Wang (12641971)

    Published 2022
    “…We mapped the hub IRGs by protein-protein interaction network (PPI) and extracted the top 10 ranked genes. …”
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  11. 171
    Image2_Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.TIFF

    Image2_Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.TIFF by Qiang-Sheng Wang (12641971)

    Published 2022
    “…We mapped the hub IRGs by protein-protein interaction network (PPI) and extracted the top 10 ranked genes. …”
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  12. 172
    Image1_Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.TIFF

    Image1_Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.TIFF by Qiang-Sheng Wang (12641971)

    Published 2022
    “…We mapped the hub IRGs by protein-protein interaction network (PPI) and extracted the top 10 ranked genes. …”
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  13. 173
    DataSheet_1_Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers.pdf

    DataSheet_1_Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers.pdf by Mark Uhlik (628328)

    Published 2023
    “…</p>Methods<p>The Panel algorithm is an artificial neural network (ANN) trained with an input signature of 124 genes that was optimized across various solid tumors. …”
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  14. 174
    Table2_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table2_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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  15. 175
    Table3_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table3_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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  16. 176
    Table1_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table1_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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  17. 177
    Table5_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table5_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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  18. 178
    Table6_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table6_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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  19. 179
    Table4_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table4_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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  20. 180
    DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx

    DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

    Published 2024
    “…Cooking data were classified into binary and multiclass variables (CT4C and CT6C). …”
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