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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 30 |
1 files changed, 26 insertions, 4 deletions
@@ -23,15 +23,35 @@ REST operations [min_frequency=5 per-mil], [feature_type=trees], [backbone=true], - [min_chisq_significance=0.95] + [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], - [max_hops=25], + [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 ---------------------- @@ -68,6 +88,7 @@ backbone=false reduces BBRC mining to frequent and correlated subtree mining (mu 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. @@ -75,15 +96,16 @@ Please click [here](http://bbrc.maunz.de#usage) for more guidance on usage. 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 - curl -X POST -d dataset_uri={datset_uri} -d prediction_feature={feature_uri} -d feature_generation_uri=http://webservices.in-silico.ch/algorithm/fminer http://webservices.in-silico.ch/test/algorithm/lazar +Creates a standard Lazar model. -feature_uri specifies the dependent variable from the dataset + 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) -------------------------------------------------------------- |