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] [activity_transform=] [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". - activity_transform: Normalizing transformations of the y-values (activities), applicable only to regression problems. One of "Log10", "Inverter", "NOP". "Log10" moves all values above zero and takes the log to base 10. "Inverter" moves all values above 1.0 and takes the inverted value. "NOP" is the identity transformation, which does nothing. Model predictions are output with reverse transformation applied. - 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.