Mercurial > repos > rnateam > graphclust_nspdk
comparison NSPDK_sparseVect.xml @ 0:165fe96228be draft
planemo upload for repository https://github.com/eteriSokhoyan/galaxytools/tree/branchForIterations/tools/GraphClust/NSPDK commit 21aaee40723b5341b4236edeb0e72995c2054053
| author | rnateam |
|---|---|
| date | Fri, 16 Dec 2016 07:36:07 -0500 |
| parents | |
| children | 90a4a2e7d876 |
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| -1:000000000000 | 0:165fe96228be |
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| 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> |