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 Get a representation of the GET /feature_selection - feature selection representation 200,404 feature selection algorithms Get a representation of the GET /feature_selection/rfe - rfe representation 200,404 rfe 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], [feature_dataset_uri], [pc_type=null], [nr_hits=false (class. using wt. maj. vote), true (else)], [min_sim=0.3 (nominal), 0.4 (numeric features)] [min_train_performance=0.1] Create selected features POST /feature_selection/rfe dataset_uri, URI for dataset 200,400,404,500 prediction_feature, feature_dataset_uri, [del_missing=false] Synopsis -------- - prediction\_algorithm: One of "weighted\_majority\_vote" (default for classification), "local\_svm\_classification", "local\_svm\_regression" (default for regression). "weighted\_majority\_vote" is not applicable for regression. - pc_type: Mandatory for feature dataset, one of [geometrical, topological, electronic, constitutional, hybrid, cpsa]. - nr_hits: Whether nominal features should be instantiated with their occurrence counts in the instances. One of "true", "false". - min_sim: The minimum similarity threshold for neighbors. Numeric value in [0,1]. - min_train_performance. The minimum training performance for "local\_svm\_classification" (Accuracy) and "local\_svm\_regression" (R-squared). Numeric value in [0,1]. - del_missing: 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) -------------------------------------------------------------- * * * ### Create a feature dataset of selected features curl -X POST -d dataset_uri={dataset_uri} -d prediction_feature_uri={prediction_feature_uri} -d feature_dataset_uri={feature_dataset_uri} -d del_missing=true http://webservices.in-silico.ch/test/algorithm/feature_selection/rfe Copyright (c) 2009-2011 Christoph Helma, Martin Guetlein, Micha Rautenberg, Andreas Maunz, David Vorgrimmler, Denis Gebele. See LICENSE for details.