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model implementation » modular implementation (Expand Search), world implementation (Expand Search), time implementation (Expand Search)
python based » method based (Expand Search), person based (Expand Search)
python model » python code (Expand Search), python tool (Expand Search), action model (Expand Search)
model implementation » modular implementation (Expand Search), world implementation (Expand Search), time implementation (Expand Search)
python based » method based (Expand Search), person based (Expand Search)
python model » python code (Expand Search), python tool (Expand Search), action model (Expand Search)
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121
CSMILES: A Compact, Human-Readable SMILES Extension for Conformations
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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122
CSMILES: A Compact, Human-Readable SMILES Extension for Conformations
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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123
Supplementary file 1_ParaDeep: sequence-based deep learning for residue-level paratope prediction using chain-aware BiLSTM-CNN models.docx
Published 2025“…Its efficiency and scalability make it well-suited for early-stage antibody discovery, repertoire profiling, and therapeutic design, particularly in the absence of structural data. The implementation is freely available at https://github.com/PiyachatU/ParaDeep, with Python (PyTorch) code and a Google Colab interface for ease of use.…”
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124
The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation"
Published 2025“…<h2>A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation</h2><p><br></p><p dir="ltr">This is the implementation for the paper "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation".…”
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125
The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation"
Published 2025“…<h2>A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation</h2><p><br></p><p dir="ltr">This is the implementation for the paper "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation".…”
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126
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128
Number of tweets collected over time.
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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129
Descriptive measures of the dataset.
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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130
Corpora from the articles in order of size.
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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131
Media information.
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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132
Information from interactions.
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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133
Table of the database statistical measures.
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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134
Tweets information.
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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135
Examples of tweets texts (Portuguese).
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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136
Methodological flowchart.
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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137
Number of tweets collected per query and type.
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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138
Examples of tweets texts (English).
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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139
Users information.
Published 2025“…Python algorithms were developed to model each primary collection type. …”
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140
The Improved Hydro-Sediment Numerical Model and Machine Learning Models
Published 2025“…The hydro-sediment model was implemented in the C# programming language using Visual Studio, while the machine learning models were developed in Python.…”