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Showing 41 - 60 results of 254 for search '(( python code implementation ) OR ( python model representing ))', query time: 0.37s Refine Results
  1. 41
    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739).

    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). by Enock Niyonkuru (21246879)

    Published 2025
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  2. 42
    Comparative analysis of WordNet replacement impact on data distribution.

    Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

    Published 2025
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  3. 43
    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10, this Table shows the results for three different window sizes at a =. Abbreviations are the same as for Table 1.

    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... by Enock Niyonkuru (21246879)

    Published 2025
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  4. 44
    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet.

    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet. by Enock Niyonkuru (21246879)

    Published 2025
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  5. 45
    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,918. The Table summarizes results for different thresholds () and categories of concept/gene sets (M,B,K,G,P). Columns: : Replacement threshold; replaced: Unique Replaced Concepts; Category: M = MeSH, B = Biocarta, K = KEGG, G = GP(bp), P = PID; # sets: Number of concept/gene sets in the categories; #Concepts: number of concept vectors in the category; <i>WN better</i><i>: The count and percentage of concept/gene sets for which the mean interconcept distance was smaller for WN than for PM. “Winners” are shown in bold.;</i> <i>PM better</i><i>: Analogous to “WN better” but for PM.</i>

    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... by Enock Niyonkuru (21246879)

    Published 2025
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  6. 46
    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of concept sets where the cluster mean distance is significantly lower, indicative of superior embeddings.

    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... by Enock Niyonkuru (21246879)

    Published 2025
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  7. 47
    Supporting data for "Interpreting complex ecological patterns and processes across differentscales using Artificial Intelligence"

    Supporting data for "Interpreting complex ecological patterns and processes across differentscales using Artificial Intelligence" by Yifei Gu (9507104)

    Published 2025
    “…<p dir="ltr">This thesis bridges the gap between field observations and the empirical understanding of ecological systems by applying AI models at different spatial scales and hierarchical levels to study ecological complexity. The detailed implementation, source code and demo dataset are included in dedicated folders for each chapter.…”
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  8. 48
    PythonForRoyalSociety.py from The formation energy of triple lines

    PythonForRoyalSociety.py from The formation energy of triple lines by Paul Twine (22587761)

    Published 2025
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  9. 49
    Steering system model.

    Steering system model. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  10. 50
    Braking system model.

    Braking system model. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  11. 51
    Code

    Code by Baoqiang Chen (21099509)

    Published 2025
    “…</p><p><br></p><p dir="ltr">For the 5′ UTR library, we developed a Python script to extract sequences and Unique Molecular Identifiers (UMIs) from the FASTQ files. …”
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  12. 52
    CSMILES: A Compact, Human-Readable SMILES Extension for Conformations

    CSMILES: A Compact, Human-Readable SMILES Extension for Conformations by James W. Furness (22319738)

    Published 2025
    “…A two-way conversion from three-dimensional (3D) structure to CSMILES has been implemented, and the article is accompanied by a Python code which effectuates such conversions. …”
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  13. 53
    CSMILES: A Compact, Human-Readable SMILES Extension for Conformations

    CSMILES: A Compact, Human-Readable SMILES Extension for Conformations by James W. Furness (22319738)

    Published 2025
    “…A two-way conversion from three-dimensional (3D) structure to CSMILES has been implemented, and the article is accompanied by a Python code which effectuates such conversions. …”
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  14. 54
    Scripts, data and figures underpinning 'Towards the Creation of Legible Octilinear Power Grid Diagrams Using Mixed Integer Linear Programming'

    Scripts, data and figures underpinning 'Towards the Creation of Legible Octilinear Power Grid Diagrams Using Mixed Integer Linear Programming' by Paul Cuffe (2937081)

    Published 2024
    “…<p dir="ltr">These Python notebooks implement the techniques described in the paper "Towards the Creation of Legible Octilinear Power Grid Diagrams Using Mixed Integer Linear Programming".…”
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  15. 55
    Procedure for aligning the NeuroRat head model underlying the FEM simulations with the BBP circuit model.

    Procedure for aligning the NeuroRat head model underlying the FEM simulations with the BBP circuit model. by Joseph James Tharayil (21416715)

    Published 2025
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  16. 56
    High-resolution images of <i>C. elegans</i> germlines shown in Fig. 5A for representative images from three genotypes (Wild-type, <i>zim-2</i>, <i>syp-4</i>) with manually annotated zones (top) and automatic classifications of nuclear morphology using the Spherical Texture Random Forest model per nucleus (bottom).

    High-resolution images of <i>C. elegans</i> germlines shown in Fig. 5A for representative images from three genotypes (Wild-type, <i>zim-2</i>, <i>syp-4</i>) with manually annotate... by Oane Gros (20636735)

    Published 2025
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  17. 57
    Fitting a feast/famine cycle to a glycolysis model.

    Fitting a feast/famine cycle to a glycolysis model. by Paul van Lent (16876977)

    Published 2025
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  18. 58
    SEPARATE Code Repository

    SEPARATE Code Repository by Gyuri Kim (21424973)

    Published 2025
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  19. 59
    Comparison of tools with features similar to <i>bmdrc,</i> and a descriptions of the modules within the <i>bmdrc</i> package. 

    Comparison of tools with features similar to <i>bmdrc,</i> and a descriptions of the modules within the <i>bmdrc</i> package.  by David J. Degnan (13886280)

    Published 2025
    “…The smaller boxes within the larger boxes represent individual functions within the <i><i>bmdrc</i></i> Python library.…”
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  20. 60
    Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , with different . The control parameter is . (B) The potential landscape is approximated by simulating a large number of molecules corresponding to concentrations ( samples) with parameter values , , and , respectively representing the adenocarcinoma state, the intermediate state, and the squamous carcinoma state. FOXA2 and SOX2 amounts are chosen to visualize the landscape. (C) Principle of SOX2-targeted degradation by the PROTAC technique. The PROTAC molecule here is designed to recognize SOX2 proteins and to recruit E3 ligases to induce an addition of ubiquitin (Ub) proteins onto SOX2, which causes degradation of SOX2 subsequently. (D) Application of a SOX2 degrader to the squamous carcinoma state () reprogram the AST network back to the adenocarcinoma state. Here the treatment effect of the SOX2 degrad

    Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , w... by Ziyan Jin (21021976)

    Published 2025
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