  Gene annotation addresses the problem of predicting unknown associations
between gene and functions (e.g., biological processes) of a specific organism.
Despite recent advances, the cost and time demanded by annotation procedures
that rely largely on in vivo biological experiments remain prohibitively high.
This paper presents a novel in silico approach for to the annotation problem
that combines cluster analysis and hierarchical multi-label classification
(HMC). The approach uses spectral clustering to extract new features from the
gene co-expression network (GCN) and enrich the prediction task. HMC is used to
build multiple estimators that consider the hierarchical structure of gene
functions. The proposed approach is applied to a case study on Zea mays, one of
the most dominant and productive crops in the world. The results illustrate how
in silico approaches are key to reduce the time and costs of gene annotation.
More specifically, they highlight the importance of: (i) building new features
that represent the structure of gene relationships in GCNs to annotate genes;
and (ii) taking into account the structure of biological processes to obtain
consistent predictions.

Machine Learning