Mercurial > repos > bgruening > graphclust_nspdk

comparison NSPDK_sparseVect.xml @ 0:28e440724de2 draft default tip

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/eteriSokhoyan/galaxytools/tree/branchForIterations/tools/GraphClust/NSPDK commit f447414150c19865e904d3914a68e2479fadddce
author bgruening
date Thu, 15 Dec 2016 18:18:52 -0500
parents
children
comparison
equal deleted inserted replaced
-1:000000000000 0:28e440724de2
1 <tool id="nspdk_sparse" name="NSPDK_sparseVect" version="9.2">
2 <requirements>
3 <requirement type="package" version="0.1">graphclust-wrappers</requirement>
4 <requirement type="package" version="9.2">nspdk</requirement>
5 </requirements>
6 <stdio>
7 <exit_code range="1:" />
8 </stdio>
9 <command>
10 <![CDATA[
11
12
13 'NSPDK_sparseVect.pl' '$data_fasta' '$gspan_file' $max_rad $max_dist_relations
14
15 ]]>
16 </command>
17 <inputs>
18 <param type="data" name="gspan_file" format="searchgui_archive" />
19 <param type="data" name="data_fasta" format="fasta" />
20 <param name="max_rad" type="integer" value="3" size="5" label="maximum radius " help="-R"/>
21 <param name="max_dist_relations" type="integer" value="3" size="5" label="maximum distance relations" help="-D"/>
22 </inputs>
23 <outputs>
24 <data name="data_svector" format="zip" from_work_dir="SVECTOR/data.svector" label="data_svector"/>
25 </outputs>
26 <tests>
27 <test>
28 <param name="data_fasta" value="data.fasta"/>
29 <param name="gspan_file" value="1.group.gspan.bz2" ftype="searchgui_archive"/>
30 <param name="max_rad" value="3"/>
31 <param name="max_dist_relations" value="3"/>
32 <output name="data_svector" file="SVECTOR/data.svector" ftype="zip" />
33 </test>
34 </tests>
35 <help>
36 <![CDATA[
37
38 **What it does**
39
40 Produces an explicit sparse feature encoding.
41 Integer code for the invariant graph encoding is used as a feature indicator. In this way,
42 the integer associated to each feature (i.e. each pair or neighborhood subgraphs of radius r whose
43 roots are at distance d) can be interpreted as the feature key and the (normalized) count of occurrences as its value.
44 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).
45
46 **Parameters**
47
48 + **-R** <max radius> (default: 1)
49 + **-D** <max distance relations> (default: 4)
50
51
52 ]]>
53 </help>
54 <citations>
55 <citation type="doi">10.1093/bioinformatics/bts224</citation>
56 <citation type="bibtex">@inproceedings{costa2010fast,
57 title={Fast neighborhood subgraph pairwise distance kernel},
58 author={Costa, Fabrizio and De Grave, Kurt},
59 booktitle={Proceedings of the 26th International Conference on Machine Learning},
60 pages={255--262},
61 year={2010},
62 organization={Omnipress}
63 }
64 </citation>
65 </citations>
66 </tool>