Combining learning and constraints for genome-wide protein annotation.

Background: The advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related entities, like all proteins of a certain genome, many candidate annotations could be inconsistent, or very unlikely, given the existing knowledge. A sound predictive framework capable of accounting for this type of constraints in making predictions could substantially contribute to the quality of machine-generated annotations at a genomic scale.

Learning in Variable-Dimensional Spaces.

This paper proposes a unified approach to learning in environments in which patterns can be represented in variable-dimension domains, which nicely includes the case in which there are missing features. The proposal is based on the representation of the environment by pointwise constraints that are shown to model naturally pattern relationships that come out in problems of information retrieval, computer vision, and related fields. The given interpretation of learning leads to capturing the truly different aspects of similarity coming from the content at different dimensions and the pattern links.