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OpenTox Algorithm
=================
- An [OpenTox](http://www.opentox.org) REST Webservice
- Implements the OpenTox algorithm API for
- lazar
- subgraph descriptor calculation (fminer)
- physico-chemical descriptor calculation (pc) for more than 300 descriptors
- feature selection (fs) using recursive feature elimination (rfe)
- See [opentox-ruby on maunz.de](http://opentox-ruby.maunz.de) for high-level workflow documentation
REST operations
---------------
DESCRIPTION TYPE ADDRESS ARGUMENTS RETURN TYPE RETURN CODE
Get a representation of the GET /lazar - lazar representation 200,404
lazar algorithm
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 /pc - URIs of algorithms 200,404
pc algorithms
Get a representation of the GET /pc/<name> - descriptor representation 200,404
pc algorithm <name>
Get a representation of the GET /fs - URIs of algorithms 200,404
fs algorithms
Get a representation of the GET /fs/rfe - rfe representation 200,404
rfe algorithm
Create lazar model POST /lazar dataset_uri, URI for lazar model 200,400,404,500
[prediction_feature],
[feature_generation_uri],
[feature_dataset_uri],
[prediction_algorithm],
[pc_type=null],
[lib=null],
[nr_hits=false (cl+wmv),
true (else)],
[min_sim=0.3 (nominal), 0.4
(numeric features)],
[min_train_performance=0.1]
Create bbrc features POST /fminer/bbrc dataset_uri, URI for feature dataset 200,400,404,500
prediction_feature,
[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
prediction_feature,
[min_frequency=8 %],
[feature_type=trees],
[nr_hits=false]
Create features POST /pc/AllDescriptors dataset_uri, URI for dataset 200,400,404,500
[pc_type=constitutional,
topological,geometrical,
electronic,cpsa,hybrid],
[lib=cdk,joelib,openbabel]
Create feature POST /pc/<name> dataset_uri URI for dataset 200,400,404,500
Select features POST /fs/rfe dataset_uri, URI for dataset 200,400,404,500
prediction_feature,
feature_dataset_uri,
[del_missing=false]
Synopsis
--------
- *del_missing*: one of
- *true*
- *false*
- *feature\_type*: Type of subgraphs when no feature dataset is supplied, one of
- *trees*
- *paths*
- *lib*: Mandatory for feature datasets that do not contain appropriate feature metadata, one of
- *cdk*
- *openbabel*
- *joelib*
- *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].
- *nr_hits*: Whether nominal features should be instantiated with their occurrence counts in the instances. One of
- *true*
- *false*
- *pc_type*: Mandatory for feature datasets that do not contain appropriate feature metadata, one of
- *geometrical*
- *topological*
- *electronic*
- *constitutional*
- *hybrid*
- *cpsa*
- *prediction\_algorithm*: One of
- *weighted\_majority\_vote* (default for classification, n.a. for regression)
- *local\_svm\_classification*
- *local\_svm\_regression* (default for regression).
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 lazar
curl http://webservices.in-silico.ch/algorithm/lazar
### Get the OWL-DL representation of fminer
curl http://webservices.in-silico.ch/algorithm/fminer
### Get the OWL-DL representation of pc
curl http://webservices.in-silico.ch/algorithm/pc
### Get the OWL-DL representation of fs
curl http://webservices.in-silico.ch/algorithm/fs
* * *
### Create lazar model
Creates a standard Lazar model with subgraph descriptors.
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
Creates a Lazar model with physico-chemical descriptors.
curl -X POST -d dataset_uri={datset_uri} -d prediction_feature={feature_uri} -d feature_dataset_uri={feature_dataset_uri} http://webservices.in-silico.ch/test/algorithm/lazar
feature_uri specifies the dependent variable from the dataset.
* * *
Creates subgraph descriptors 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.
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.
Click [here](http://last-pm.maunz.de#usage) for guidance for more guidance on usage.
* * *
### Create a feature dataset of physico-chemical descriptors with CDK
curl -X POST -d dataset_uri={dataset_uri} -d lib=cdk http://webservices.in-silico.ch/test/algorithm/pc/AllDescriptors
lib specifies the library to use.
* * *
### Select features from a feature dataset
curl -X POST -d dataset_uri={dataset_uri} -d prediction_feature={feature_uri} -d feature_dataset_uri={feature_dataset_uri} http://webservices.in-silico.ch/test/algorithm/fs/rfe
feature_uri specifies the dependent variable from the dataset.
* * *
Copyright (c) 2009-2011 Christoph Helma, Martin Guetlein, Micha Rautenberg, Andreas Maunz, David Vorgrimmler, Denis Gebele. See LICENSE for details.
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