Mercurial > repos > jay > pdaug_word_vector_descriptor
annotate PDAUG_Word_Vector_Descriptor/PDAUG_Word_Vector_Descriptor.xml @ 1:a196bbbc7471 draft
"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit 3c91f421d26c8f42cf2671e47db735d2cf69dde8"
author | jay |
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date | Tue, 29 Dec 2020 04:40:30 +0000 |
parents | a3a1d9bea1ad |
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"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit a9bd83f6a1afa6338cb6e4358b63ebff5bed155e"
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1 <tool id="pdaug_word_vector_descriptor" name="PDAUG Word Vector Descriptor" version="0.1.0" python_template_version="3.7"> |
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2 <description>Calculates word vector descriptor</description> |
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3 |
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4 <requirements> |
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5 <requirement type="package" version="3.5">nltk</requirement> |
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6 <requirement type="package" version="1.0.3">pandas</requirement> |
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7 <requirement type="package" version="1.76">biopython</requirement> |
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8 <requirement type="package" version="3.8.0">gensim</requirement> |
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9 </requirements> |
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10 |
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11 <command detect_errors="exit_code"><![CDATA[ |
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12 |
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13 python '$__tool_directory__/PDAUG_Word_Vector_Descriptor.py' -I '$Input' -M '$ModelInput' -R '$row' -P '$positive' -N '$negative' -O '$output' |
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14 |
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15 ]]></command> |
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16 |
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17 <inputs> |
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18 <param name="ModelInput" type="data" label="Input model file" value="model.txt" format="txt" argument= "--ModelInput" help="Input word Vector model file"/> |
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19 <param name="Input" type="data" label="Training data" format="fasta" argument= "--InputFasta" help="Input tabular data file "/> |
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20 <param name="row" type="integer" label="Number of traning samples" value="200" argument= "--row" help="Number of positive and negative samples"/> |
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21 <param name="positive" type="integer" label="Positives samples" value="100" argument= "--positive" help="Number of positive samples"/> |
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22 <param name="negative" type="integer" label="Negative samples" value="100" argument= "--negative" help="Number of ngative samples"/> |
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23 </inputs> |
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24 |
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25 <outputs> |
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26 <data name='output' format='tabular' label="${tool.name} on $on_string - (tabular)" /> |
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27 </outputs> |
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28 |
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29 <tests> |
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30 <test> |
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31 <param name="Input" value="test.fasta" /> |
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32 <param name="ModelInput" value="model.txt" /> |
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33 <param name="row" value="276" /> |
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34 <param name="positive" value="138" /> |
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35 <param name="negative" value="138" /> |
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36 <output name="output" value="Out.tsv" /> |
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37 </test> |
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38 </tests> |
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39 |
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40 <help><![CDATA[ |
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41 .. class:: infomark |
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42 |
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43 **What it does** |
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44 |
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45 Calculates word vector based descriptors derived from the Skip-gram model. |
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46 |
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47 ----- |
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48 |
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49 **Inputs** |
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50 * **--InputFasta** A fasta file with negative and positive samples. |
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51 * **--ModelInput** Skip-gram model as ".txt" file generated from "Word_Vector_Model". |
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52 * **--row** Number of training samples. |
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53 * **--positive** Positive samples. |
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54 * **--negative** Negative samples. |
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55 |
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56 ----- |
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57 |
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58 **Outputs** |
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59 * **--OutFile** Tabular file with descriptor data.]]></help> |
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60 <citations> |
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61 |
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62 <citation type="bibtex"> |
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63 @misc{PDAUGGITHUB, |
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64 author = {Joshi, Jayadev and Blankenberg, Daniel}, |
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65 year = {2020}, |
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66 title ={PDAUG - a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling}, |
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67 publisher = {GitHub}, |
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68 journal = {GitHub repository}, |
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69 url = |
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70 {https://github.com/jaidevjoshi83/pdaug.git}, |
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71 } |
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72 </citation> |
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73 |
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74 <citation type="bibtex"> |
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75 @inproceedings{rehurek_lrec, |
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76 title = {{Software Framework for Topic Modelling with Large Corpora}}, |
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77 author = {Radim {\v R}eh{\r u}{\v r}ek and Petr Sojka}, |
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78 booktitle = {{Proceedings of the LREC 2010 Workshop on New |
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79 Challenges for NLP Frameworks}}, |
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80 pages = {45--50}, |
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81 year = 2010, |
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82 month = May, |
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83 day = 22, |
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84 publisher = {ELRA}, |
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85 address = {Valletta, Malta}, |
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86 url={http://is.muni.cz/publication/884893/en}, |
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87 language={English} |
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88 } |
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89 </citation> |
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90 |
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91 <citation type="bibtex"> |
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92 @article{Md_Nafiz, |
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93 title= {Identifying antimicrobial peptides using word embedding with deep recurrent neural networks}, |
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94 volume={35}, |
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95 DOI={https://doi.org/10.1093/bioinformatics/bty937}, |
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96 issue={12}, |
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97 year={2018}, |
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98 pages={2009-2016}, |
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99 journal={Europe PMC}, |
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100 author={Hamid, Md-Nafiz and Friedberg, Iddo} |
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101 } |
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102 |
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103 </citation> |
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104 |
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105 </citations> |
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106 </tool> |