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], [nr_hits=false] Create last features POST /fminer/last dataset_uri, URI for feature dataset 200,400,404,500 feature_uri, [min_frequency=8 %], [feature_type=trees], [nr_hits=false] Create lazar model POST /lazar dataset_uri, URI for lazar model 200,400,404,500 prediction_feature, feature_generation_uri prediction_algorithm [local_svm_kernel=weighted_tanimoto] [min_sim=0.3] [nr_hits=false] [conf_stdev=false] Synopsis -------- - prediction\_algorithm: One of "weighted\_majority\_vote" (default for classification), "local\_svm\_classification", "local\_svm\_regression (default for regression)", "local\_mlr\_prop". "weighted\_majority\_vote" is not applicable for regression. "local\_mlr\_prop" is not applicable for classification. - local\_svm\_kernel: One of "weighted\_tanimoto", "propositionalized". local\_svm\_kernel is not appplicable when prediction\_algorithm="weighted\_majority\_vote". - min_sim: The minimum similarity threshold for neighbors. Numeric value in [0,1]. - nr_hits: Whether for instantiated models (local\_svm\_kernel = "propositionalized" for prediction_algorithm="local\_svm\_classification" or "local\_svm\_regression", or for prediction_algorithm="local\_mlr\_prop") nominal features should be instantiated with their occurrence counts in the instances. For non-instantiated models (local\_svm\_kernel = "weighted\_tanimoto" for prediction_algorithm="local\_svm\_classification" or "local\_svm\_regression", or for prediction_algorithm="weighted\_majority\_vote") the neighbor-to-neighbor and neighbor-to-query similarity also integrates these counts, when the parameter is set. One of "true", "false". - conf_stdev: Whether confidence integrates distribution of neighbor activity values. When "true", the exp(-1.0*(standard deviation of neighbor activities)) is multiplied on the similarity. One of "true", "false". See http://www.maunz.de/wordpress/opentox/2011/lazar-models-and-how-to-trigger-them for a graphical overview. 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. Adding -d nr_hits=true produces frequency counts per pattern and molecule. 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. Adding -d nr_hits=true produces frequency counts per pattern and molecule. Please click [here](http://last-pm.maunz.de#usage) for guidance for more guidance on usage. * * * ### Create lazar model Creates a standard 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/bbrc http://webservices.in-silico.ch/test/algorithm/lazar [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.