Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions

Hierarchical Multi-Label Classification is a complex classification problem where the classes are hierarchically structured. This task is very common in protein function prediction, where each protein can have more than one function, which in turn can have more than one sub-function. In this paper, we propose a novel hierarchical multi-label classification algorithm for protein function prediction, namely HMCPC. It is based on probabilistic clustering, and it makes use of cluster membership probabilities in order to generate the predicted class vector. We perform an extensive empirical analysis in which we compare our new approach to four different hierarchical multi-label classification algorithms, in protein function datasets structured both as trees and directed acyclic graphs. We show that HMC-PC achieves superior or comparable results compared to the state-of-the-art method for hierarchical multi-label classification.