Mercurial > repos > jay > pdaug_word_vector_model

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"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit a9bd83f6a1afa6338cb6e4358b63ebff5bed155e"
author jay
date Wed, 28 Oct 2020 02:21:16 +0000
parents
children c6a1b09d8846
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1 <tool id="pdaug_word_vector_model" name="PDAUG Word Vector Model" python_template_version="3.7" version="0.1.0">
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2 <description>Generates the skip-gram model</description>
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3 <requirements>
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4 <requirement type="package" version="1.0.3">pandas</requirement>
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5 <requirement type="package" version="1.76">biopython</requirement>
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6 <requirement type="package" version="3.5">nltk</requirement>
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7 <requirement type="package" version="3.8.0">gensim</requirement>
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8 <requirement type="package" version="0.23.1">scikit-learn</requirement>
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9 <requirement type="package" version="1.18.4">numpy</requirement>
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10 </requirements>
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11 <stdio>
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12 <exit_code range="1" level="fatal" />
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13 </stdio>
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14 <command detect_errors="exit_code"><![CDATA[
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16 python '$__tool_directory__/PDAUG_Word_Vector_Model.py' -I '$input' -M '$meanCount' -W '$window' -O '$OutFile'
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18 ]]></command>
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20 <inputs>
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21 <param name="input" type="data" label="Input fasta file" format="fasta" argument= "--Input" help="Input fasta file with peptides"/>
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22 <param name="meanCount" type="integer" label="Mean Count" value="0" format="fasta" argument= "--min_count" help="Ignores a all words with total frequency lower than this"/>
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23 <param name="window" type="integer" label="window" value="5" argument="--window" help="Maximum distance between the current and predicted word within a sentence"/>
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24 </inputs>
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25
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26 <outputs>
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27 <data name='OutFile' format='txt' label="${tool.name} on $on_string - (text)" />
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28 </outputs>
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29
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30 <tests>
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31 <test>
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32 <param name="input" value="test.fasta"/>
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33 <param name="meanCount" value="0"/>
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34 <param name="window" value="5"/>
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35 <output name="OutFile" value="model.txt" lines_diff="2268" />
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36 </test>
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37 </tests>
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38 <help><![CDATA[
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39 .. class:: infomark
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40
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41 **What it does**
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42
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43 This tool calculates the skip-gram model which is a neural network where the inputs and outputs of the network are one-hot vectors calculated based on training data that contains input word and output word. 
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44
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45 -----
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46
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47 **Inputs**
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48 * **--Input** Fasta file with protein sequences.
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49 * **--min_count** Ignores all words with total frequency lower than this
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50 * **--window** Maximum distance between the current and predicted word within a sentence, accepts integer value.
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51
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52 -----
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53
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54 **Outputs**
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55 * **--OutFile** Return "model.txt" model file.
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56
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57 ]]></help>
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58
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59 <citations>
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60
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61 <citation type="bibtex">
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62 @misc{PDAUGGITHUB,
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63 author = {Joshi, Jayadev and Blankenberg, Daniel},
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64 year = {2020},
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65 title ={PDAUG - a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling},
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66 publisher = {GitHub},
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67 journal = {GitHub repository},
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68 url =
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69 {https://github.com/jaidevjoshi83/pdaug.git},
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70 }
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71 </citation>
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72
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73 <citation type="bibtex">
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74 @inproceedings{rehurek_lrec,
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75 title = {{Software Framework for Topic Modelling with Large Corpora}},
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76 author = {Radim {\v R}eh{\r u}{\v r}ek and Petr Sojka},
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77 booktitle = {{Proceedings of the LREC 2010 Workshop on New
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78 Challenges for NLP Frameworks}},
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79 pages = {45--50},
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80 year = 2010,
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81 month = May,
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82 day = 22,
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83 publisher = {ELRA},
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84 address = {Valletta, Malta},
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85 url={http://is.muni.cz/publication/884893/en},
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86 language={English}
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87 }
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88 </citation>
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89
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90 <citation type="bibtex">
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91 @article{Md_Nafiz,
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92 title= {Identifying antimicrobial peptides using word embedding with deep recurrent neural networks},
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93 volume={35},
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94 DOI={https://doi.org/10.1093/bioinformatics/bty937},
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95 issue={12},
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96 year={2018},
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97 pages={2009-2016},
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98 journal={Europe PMC},
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99 author={Hamid, Md-Nafiz and Friedberg, Iddo}
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100 }
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101
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102 </citation>
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103 </citations>
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104 </tool>
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105
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106
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107