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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]
[activity_transform=<Log10 (regression),NOP (classification)>]
[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.
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