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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.
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