Mercurial > repos > jay > pdaug_word_vector_model
annotate PDAUG_Word_Vector_Model/PDAUG_Word_Vector_Model.xml @ 6:d739a3bc7e39 draft
"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit 6da51897c98def51bc7203303b54218207d59bce"
author | jay |
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date | Fri, 29 Jan 2021 19:48:02 +0000 |
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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 word vector model for descriptor calculation</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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15 |
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16 python '$__tool_directory__/PDAUG_Word_Vector_Model.py' -I '$input' -M '$meanCount' -W '$window' -O '$OutFile' --SG '$sg' |
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17 |
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18 ]]></command> |
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19 |
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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 <param name="sg" type="select" label="Select algorithm" help="Training algorithm skip-gram or CBOW."> |
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25 <option value="skip-gram">Skip-gram</option> |
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26 <option value="CBOW" >CBOW</option> |
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27 </param> |
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28 |
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29 </inputs> |
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30 |
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31 <outputs> |
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32 <data name='OutFile' format='txt' label="${tool.name} on $on_string - (text)" /> |
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33 </outputs> |
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34 |
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35 <tests> |
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36 <test> |
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37 <param name="input" value="test.fasta"/> |
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38 <param name="meanCount" value="0"/> |
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39 <param name="window" value="5"/> |
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40 <output name="OutFile" value="model.txt" lines_diff="2268" /> |
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41 </test> |
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42 </tests> |
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43 <help><![CDATA[ |
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44 .. class:: infomark |
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45 |
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46 **What it does** |
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47 |
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48 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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49 |
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50 ----- |
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51 |
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52 **Inputs** |
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53 * **--Input** Fasta file with protein sequences. |
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54 * **--min_count** Ignores all words with total frequency lower than this |
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55 * **--window** Maximum distance between the current and predicted word within a sentence, accepts integer value. |
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56 * **--SG** Select training algorithm skip-gram or CBOW. |
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57 |
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58 ----- |
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59 |
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60 **Outputs** |
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61 * **--OutFile** Return "model.txt" model file. |
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62 |
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63 ]]></help> |
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64 |
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65 <citations> |
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66 |
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67 <citation type="bibtex"> |
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68 @misc{PDAUGGITHUB, |
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69 author = {Joshi, Jayadev and Blankenberg, Daniel}, |
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70 year = {2020}, |
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71 title ={PDAUG - a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling}, |
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72 publisher = {GitHub}, |
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73 journal = {GitHub repository}, |
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74 url = |
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75 {https://github.com/jaidevjoshi83/pdaug.git}, |
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76 } |
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77 </citation> |
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78 |
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79 <citation type="bibtex"> |
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80 @inproceedings{rehurek_lrec, |
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81 title = {{Software Framework for Topic Modelling with Large Corpora}}, |
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82 author = {Radim {\v R}eh{\r u}{\v r}ek and Petr Sojka}, |
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83 booktitle = {{Proceedings of the LREC 2010 Workshop on New |
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84 Challenges for NLP Frameworks}}, |
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85 pages = {45--50}, |
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86 year = 2010, |
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87 month = May, |
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88 day = 22, |
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89 publisher = {ELRA}, |
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90 address = {Valletta, Malta}, |
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91 url={http://is.muni.cz/publication/884893/en}, |
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92 language={English} |
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93 } |
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94 </citation> |
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95 |
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96 <citation type="bibtex"> |
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97 @article{Md_Nafiz, |
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98 title= {Identifying antimicrobial peptides using word embedding with deep recurrent neural networks}, |
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99 volume={35}, |
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100 DOI={https://doi.org/10.1093/bioinformatics/bty937}, |
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101 issue={12}, |
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102 year={2018}, |
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103 pages={2009-2016}, |
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104 journal={Europe PMC}, |
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105 author={Hamid, Md-Nafiz and Friedberg, Iddo} |
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106 } |
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107 |
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108 </citation> |
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109 </citations> |
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110 </tool> |
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111 |
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112 |
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113 |