Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity.

ABSTRACT:

Background

A new algorithm has been developed to enable the interpretation of black box models. The developed algorithm is agnostic to learning algorithm and open to all structural based descriptors such as fragments, keys and hashed fingerprints. The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.A fragmentation algorithm is utilised to investigate the model's behaviour on specific substructures present in the query. An output is formulated summarising causes of activation and deactivation. The algorithm is able to identify multiple causes of activation or deactivation in addition to identifying localised deactivations where the prediction for

SUBMITTER: Webb SJ 

PROVIDER: S-EPMC3997921 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature