Mercurial > repos > rnateam > graphclust_nspdk

annotate NSPDK_sparseVect.xml @ 3:2a7f99a14b5f draft

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planemo upload for repository https://github.com/eteriSokhoyan/galaxytools/tree/branchForIterations/tools/GraphClust/NSPDK commit b'7e5cd452018ae9507c2d1cd13dd688a747550393\n'
author rnateam
date Fri, 17 Mar 2017 08:24:36 -0400
parents 062be03c293f
children a0bfb7782af1
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1 <tool id="nspdk_sparse" name="NSPDK_sparseVect" version="9.2">
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2 <requirements>
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3 <requirement type="package" version="0.1.11">graphclust-wrappers</requirement>
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4 <requirement type="package" version="9.2">nspdk</requirement>
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5 </requirements>
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6 <stdio>
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7 <exit_code range="1:" />
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8 </stdio>
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9 <command>
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10 <![CDATA[
0
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11
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12 NSPDK_sparseVect.pl
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13 '$data_fasta'
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14 '$gspan_file'
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15 $max_rad
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16 $max_dist_relations
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18 ]]>
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19 </command>
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20 <inputs>
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21 <param type="data" name="gspan_file" format="txt" label="gspan file"/>
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22 <param type="data" name="data_fasta" format="fasta" />
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23 <param name="max_rad" type="integer" value="3" label="maximum radius " help="-R"/>
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24 <param name="max_dist_relations" type="integer" value="3" label="maximum distance relations" help="-D"/>
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25 </inputs>
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26 <outputs>
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27 <data name="data_svector" format="zip" from_work_dir="SVECTOR/data.svector" label="data_svector"/>
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28 </outputs>
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29 <tests>
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30 <test>
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31 <param name="data_fasta" value="data.fasta"/>
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32 <param name="gspan_file" value="1.group.gspan.bz2" ftype="searchgui_archive"/>
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33 <param name="max_rad" value="3"/>
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34 <param name="max_dist_relations" value="3"/>
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35 <output name="data_svector" file="SVECTOR/data.svector" ftype="zip" />
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36 </test>
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37 </tests>
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38 <help>
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39 <![CDATA[
0
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d7d270d97c25 planemo upload for repository https://github.com/eteriSokhoyan/galaxytools/tree/branchForIterations/tools/GraphClust/NSPDK commit 21aaee40723b5341b4236edeb0e72995c2054053
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41 **What it does**
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43 Produces an explicit sparse feature encoding.
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44 Integer code for the invariant graph encoding is used as a feature indicator. In this way,
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45 the integer associated to each feature (i.e. each pair or neighborhood subgraphs of radius r whose
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46 roots are at distance d) can be interpreted as the feature key and the (normalized) count of occurrences as its value.
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47 This allows to obtain an explicit feature encoding for a given graph G as a sparse vector in ℝ^m (with a very high dimension m).
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49 **Parameters**
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50
2
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51 + **-R** <max radius> (default: 1)
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52 + **-D** <max distance relations> (default: 4)
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53
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54
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55 ]]>
2
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56 </help>
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57 <citations>
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58 <citation type="doi">10.1093/bioinformatics/bts224</citation>
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59 <citation type="bibtex">@inproceedings{costa2010fast,
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60 title={Fast neighborhood subgraph pairwise distance kernel},
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61 author={Costa, Fabrizio and De Grave, Kurt},
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62 booktitle={Proceedings of the 26th International Conference on Machine Learning},
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63 pages={255--262},
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64 year={2010},
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65 organization={Omnipress}
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66 }
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67 </citation>
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68 </citations>
0
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69 </tool>