Mercurial > repos > jay > pdaug_word_vector_model
comparison PDAUG_Word_Vector_Model/PDAUG_Word_Vector_Model.xml @ 0:3ce435b8d648 draft
"planemo upload for repository https://github.com/jaidevjoshi83/pdaug commit a9bd83f6a1afa6338cb6e4358b63ebff5bed155e"
author | jay |
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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"> | |
2 <description>Generates the skip-gram model</description> | |
3 <requirements> | |
4 <requirement type="package" version="1.0.3">pandas</requirement> | |
5 <requirement type="package" version="1.76">biopython</requirement> | |
6 <requirement type="package" version="3.5">nltk</requirement> | |
7 <requirement type="package" version="3.8.0">gensim</requirement> | |
8 <requirement type="package" version="0.23.1">scikit-learn</requirement> | |
9 <requirement type="package" version="1.18.4">numpy</requirement> | |
10 </requirements> | |
11 <stdio> | |
12 <exit_code range="1" level="fatal" /> | |
13 </stdio> | |
14 <command detect_errors="exit_code"><![CDATA[ | |
15 | |
16 python '$__tool_directory__/PDAUG_Word_Vector_Model.py' -I '$input' -M '$meanCount' -W '$window' -O '$OutFile' | |
17 | |
18 ]]></command> | |
19 | |
20 <inputs> | |
21 <param name="input" type="data" label="Input fasta file" format="fasta" argument= "--Input" help="Input fasta file with peptides"/> | |
22 <param name="meanCount" type="integer" label="Mean Count" value="0" format="fasta" argument= "--min_count" help="Ignores a all words with total frequency lower than this"/> | |
23 <param name="window" type="integer" label="window" value="5" argument="--window" help="Maximum distance between the current and predicted word within a sentence"/> | |
24 </inputs> | |
25 | |
26 <outputs> | |
27 <data name='OutFile' format='txt' label="${tool.name} on $on_string - (text)" /> | |
28 </outputs> | |
29 | |
30 <tests> | |
31 <test> | |
32 <param name="input" value="test.fasta"/> | |
33 <param name="meanCount" value="0"/> | |
34 <param name="window" value="5"/> | |
35 <output name="OutFile" value="model.txt" lines_diff="2268" /> | |
36 </test> | |
37 </tests> | |
38 <help><![CDATA[ | |
39 .. class:: infomark | |
40 | |
41 **What it does** | |
42 | |
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. | |
44 | |
45 ----- | |
46 | |
47 **Inputs** | |
48 * **--Input** Fasta file with protein sequences. | |
49 * **--min_count** Ignores all words with total frequency lower than this | |
50 * **--window** Maximum distance between the current and predicted word within a sentence, accepts integer value. | |
51 | |
52 ----- | |
53 | |
54 **Outputs** | |
55 * **--OutFile** Return "model.txt" model file. | |
56 | |
57 ]]></help> | |
58 | |
59 <citations> | |
60 | |
61 <citation type="bibtex"> | |
62 @misc{PDAUGGITHUB, | |
63 author = {Joshi, Jayadev and Blankenberg, Daniel}, | |
64 year = {2020}, | |
65 title ={PDAUG - a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling}, | |
66 publisher = {GitHub}, | |
67 journal = {GitHub repository}, | |
68 url = | |
69 {https://github.com/jaidevjoshi83/pdaug.git}, | |
70 } | |
71 </citation> | |
72 | |
73 <citation type="bibtex"> | |
74 @inproceedings{rehurek_lrec, | |
75 title = {{Software Framework for Topic Modelling with Large Corpora}}, | |
76 author = {Radim {\v R}eh{\r u}{\v r}ek and Petr Sojka}, | |
77 booktitle = {{Proceedings of the LREC 2010 Workshop on New | |
78 Challenges for NLP Frameworks}}, | |
79 pages = {45--50}, | |
80 year = 2010, | |
81 month = May, | |
82 day = 22, | |
83 publisher = {ELRA}, | |
84 address = {Valletta, Malta}, | |
85 url={http://is.muni.cz/publication/884893/en}, | |
86 language={English} | |
87 } | |
88 </citation> | |
89 | |
90 <citation type="bibtex"> | |
91 @article{Md_Nafiz, | |
92 title= {Identifying antimicrobial peptides using word embedding with deep recurrent neural networks}, | |
93 volume={35}, | |
94 DOI={https://doi.org/10.1093/bioinformatics/bty937}, | |
95 issue={12}, | |
96 year={2018}, | |
97 pages={2009-2016}, | |
98 journal={Europe PMC}, | |
99 author={Hamid, Md-Nafiz and Friedberg, Iddo} | |
100 } | |
101 | |
102 </citation> | |
103 </citations> | |
104 </tool> | |
105 | |
106 | |
107 |