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.