1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
|
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.
|
