Knowledge discovery with Artificial Immune Systems for hierarchical multi-label classification of protein functions

This work presents a system for knowledge discovery from protein databases, based on an Artificial Immune System. The discovered rules have the advantage of representing comprehensible knowledge to biologist users. This task leads to a very challenging problem since a protein can be assigned multiple classes (functions or Gene Ontology (GO) terms) across several levels of the GO's term hierarchy. To solve this problem we present two versions of an algorithm called MHC-AIS (Multi-label Hierarchical Classification with an Artificial Immune System), which is a sophisticated classification algorithm tailored to both multi-label and hierarchical classification. The first version of MHC-AIS builds a global classifier to predict all classes in the dataset, whilst the second version builds a local classifier to predict each class. The proposed versions and an algorithm chosen for comparison are evaluated on a protein dataset, and the results show that MHC-AIS outperformed the compared algorithm in general.