New Ideas for Applying Ant Colony Optimization to the Protein Function
G. Mohana Prabha, S. Chitra
Journal of Data Processing, Vol. 8, No. 2, 2018.
DOI: 10.6025/jdp/2018/8/2/74-81
Link: http://www.dline.info/jdp/fulltext/v8n2/jdpv8n2_3.pdf
Abstract:
A novel Ant Colony Optimization algorithm (ACO) is combined for the hierarchical multi- label classification problem of protein function prediction. This kind of problem is mainly focused on biometric area, given the large increase in
the number of uncharacterized proteins available for analysis and the importance of determining their functions in order to
improve the current biological knowledge. Because it is known that a protein can perform more than one function and many
protein functional-definition schemes are organized in a hierarchical structure, the classification problem in this case is an
instance of a hierarchical multi-label problem. In this classification method, each class might have multiple class labels and
class labels are represented in a hierarchical structureâ€”either a tree or a directed acyclic graph (DAG) structure. A more
difficult problem than conventional flat classification in this approach, given that the classification algorithm has to take
into account hierarchical relationships between class labels and be able to predict multiple class labels for the same example. The proposed ACO algorithm discovers an ordered list of hierarchical multi-label classification rules.