Dissimilarity space representations and automatic feature selection for protein function prediction

Dissimilarity spaces, along with feature reduction/ selection techniques, are among the mainstream approaches when dealing with pattern recognition problems in structured (and possibly non-metric) domains. In this work, we aim at investigating dissimilarity space representations in a biology-related application, namely protein function classification, as proteins are a seminal example of structured data given their primary and tertiary structures. Specifically, we propose two different analyses relying on both the complete dissimilarity matrix and a dimensionally-reduced version of the complete dissimilarity matrix, thereby casting the pattern recognition problem from structured domains towards real-valued feature vectors, for which any standard classification algorithm can be used. A third, hybrid, analysis uses a clustering-based one-class classifier exploiting different representations. First results conducted on a subset of the Escherichia coli proteome are promising and some of the analyses presented in this work may also dually suit field-experts, further bridging the gap between natural sciences and computational intelligence techniques.