ToxCreate

Issue Tracker: An issue tracker is available online at : http://github.com/sopasakis/yaqp/issues (Hosted by github).

Documentation/Examples: Detailed documentation and examples of use can be found at the new location of our web site which is still under development.

Training of Regression Models

Use Case: Training of Regression Models; Build a model that predicts a numeric value for a certain feature

Assumes: Dataset Services, Dataset with at least two numeric features one of which should be declared to be the predicted one.

Intended Audience: Scientists related to life sciences and toxicology, QSAR experts, People intrested in machine learning/statistics, Pharmaceutical Industry R&D and other related fields.

Input Information Needed:

• URI of an existing dataset provided by a related service including at least two numeric features.
• The target feature need to be declared.
• Other model specific parameters are used to calibrate the algorithm that produces the model (for instance, the parameter γ in the SVM case.)

Exception Events:

• Provided dataset URI does not exist
• Unacceptable tuning parameters (for example γ < 0 or tolerance < 0)
• Provided prediction feature is not a valid feature of the dataset
• Dataset contains less than two numeric attributes
• The provided prediction feature is not numeric in this dataset

Expected Result: URI of trained model

Subsequent Events: Once the model is generated the following use cases can include it:

• Use the model for prediction
• Validate the model using the training data or other external data

List of Model Training Services:

• http://opentox.ntua.gr:3000/algorithm  [Provides a list of all algorithms on the server]
• http://opentox.ntua.gr:3000/algorithm/svm
• http://opentox.ntua.gr:3000/algorithm/mlr

Training of Classification Models

Use Case: Training of Classification Models; Build a model that predicts a nominal value (a category) for a certain feature. Nominal are called those features that accept values in a finite set of values, not necessarily numeric

Assumes: Dataset Services, Dataset with at least one nominal feature and another numeric or nominal feature

Intended Audience: Scientists related to life sciences and toxicology, QSAR experts, People intrested in machine learning/statistics, Pharmaceutical Industry R&D and other related fields.

Input Information Needed:

• URI of an existing dataset provided by the user, as described in the 'Assumes' section
• The target feature need to be declared.
• Other model specific parameters are used to calibrate the algorithm that produces the model (for instance, the parameter γ in the case of support vector classifiers.)

Exception Events:

• Provided dataset URI does not exist
• Unacceptable tuning parameters (for example γ < 0 or tolerance < 0)
• Provided prediction feature is not a valid feature of (or it is not contained in) the dataset
• Dataset is not valid for classification.
• The provided prediction feature is not nominal in this dataset

Expected Result: URI of trained model

Subsequent Events: Once the model is generated the following use cases can exploit it:

• Use the model for prediction
• Validate the model using the training data or other external data

List of Model Training Services:

Domain of Applicability

Use Case: Domain of Applicability Calculation Services; Build a resource (proposal: a model-type resource; needs to be agreed) that is able to decide whether a compound or a set of such can be used in combination with a certain model, or as it is formally said, whether a certain compound is in the domain of applicability of a certain model.

Assumes: Dataset and Model services

Intended Audience: Scientists related to life sciences and toxicology, QSAR experts, People intrested in machine learning/statistics, Pharmaceutical Industry R&D and other related fields.

Input Information Needed:

• URI of an existing dataset provided by the user, as described in the 'Assumes' section OR
• URI of existing compound and a set of services able to calculate the features for this compound which are independent features in the model under consideration
• URI of a trained model

Exception Events:

• Compound URI or Dataset URI not found
• Features could not be calculated for a given compound
• The provided dataset does not contain the independent features of the model whose DoA is to be calculated
• Unacceptable tuning parameters (if present)

Expected Result: URI of DoA model

Subsequent Events: Once the DoA model is generated the following use cases can exploit it:

• Use the DoA model to tell whether a prediction model is appropriate for the prediction concerning a certain compound
• Use the DoA model to find one or more appropriate models for a certain compound or dataset

List of DoA Services:

Data CleanUp Services

Use Case: Data Preprocessing services used to clean dataset from unwanted features (e.g. String) and/or missing values. Mainly we recognize two types of cleanup services: One that removes all features of a certain type from a dataset thus creating a new one and services that compensate for missing values, substituting them with the mean or median value of all other feature values for the same feature.

Assumes: Dataset services, Available datasets

Intended Audience: Scientists related to life sciences and toxicology, QSAR experts, People intrested in machine learning/statistics, Pharmaceutical Industry R&D and other related fields.

Input Information Needed:

• URI of an existing dataset provided by the user
• Service-specific parameters such as the type of cleanup to be applied to the dataset.