OpenTox Algorithm ================= - An [OpenTox](http://www.opentox.org) REST Webservice - Implements the OpenTox algorithm API for - fminer - lazar REST operations --------------- Get a list of all algorithms GET / - URIs of algorithms 200 Get a representation of the GET /fminer/ - fminer representation 200,404 fminer algorithms Get a representation of the GET /fminer/bbrc - bbrc representation 200,404 bbrc algorithm Get a representation of the GET /fminer/last - last representation 200,404 last algorithm Get a representation of the GET /lazar - lazar representation 200,404 lazar algorithm Create bbrc features POST /fminer/bbrc dataset_uri, URI for feature dataset 200,400,404,500 feature_uri, [min_frequency=5 per-mil], [feature_type=trees], [backbone=true], [min_chisq_significance=0.95] Create last features POST /fminer/last dataset_uri, URI for feature dataset 200,400,404,500 feature_uri, [min_frequency=8 %], [feature_type=trees], [max_hops=25], Create lazar model POST /lazar dataset_uri, URI for lazar model 200,400,404,500 prediction_feature, feature_generation_uri Supported MIME formats ---------------------- - application/rdf+xml (default): read/write OWL-DL - application/x-yaml: read/write YAML Examples -------- NOTE: http://webservices.in-silico.ch hosts the stable version that might not have complete functionality yet. **Please try http://ot-test.in-silico.ch** for latest versions. ### Get the OWL-DL representation of fminer curl http://webservices.in-silico.ch/algorithm/fminer ### Get the OWL-DL representation of lazar curl http://webservices.in-silico.ch/algorithm/lazar * * * The following creates datasets with backbone refinement class representatives or latent structure patterns, using supervised graph mining, see http://cs.maunz.de. These features can be used e.g. as structural alerts, as descriptors (fingerprints) for prediction models or for similarity calculations. ### Create the full set of frequent and significant subtrees curl -X POST -d dataset_uri={datset_uri} -d prediction_feature={feature_uri} -d min_frequency={min_frequency} -d "backbone=false" http://webservices.in-silico.ch/algorithm/fminer/bbrc feature_uri specifies the dependent variable from the dataset. backbone=false reduces BBRC mining to frequent and correlated subtree mining (much more descriptors are produced). ### Create [BBRC](http://bbrc.maunz.de) features, recommended for large and very large datasets. curl -X POST -d dataset_uri={datset_uri} -d prediction_feature={feature_uri} -d min_frequency={min_frequency} http://webservices.in-silico.ch/algorithm/fminer/bbrc feature_uri specifies the dependent variable from the dataset. Please click [here](http://bbrc.maunz.de#usage) for more guidance on usage. ### Create [LAST-PM](http://last-pm.maunz.de) descriptors, recommended for small to medium-sized datasets. curl -X POST -d dataset_uri={datset_uri} -d prediction_feature={feature_uri} -d min_frequency={min_frequency} http://webservices.in-silico.ch/algorithm/fminer/last feature_uri specifies the dependent variable from the dataset. Please click [here](http://last-pm.maunz.de#usage) for guidance for more guidance on usage. * * * ### Create lazar model curl -X POST -d dataset_uri={datset_uri} -d prediction_feature={feature_uri} -d feature_generation_uri=http://webservices.in-silico.ch/algorithm/fminer http://webservices.in-silico.ch/test/algorithm/lazar feature_uri specifies the dependent variable from the dataset [API documentation](http://rdoc.info/github/opentox/algorithm) -------------------------------------------------------------- Copyright (c) 2009-2011 Christoph Helma, Martin Guetlein, Micha Rautenberg, Andreas Maunz, David Vorgrimmler, Denis Gebele. See LICENSE for details.