INVESTIGADORES
CARRARI Fernando Oscar
artículos
Título:
A Biologically-inspired Validity Measure for Comparison of Clustering Methods over Metabolic Datasets.
Autor/es:
STEGMAYER G; MILONE D; KAMENETZKY L; LOPEZ M; CARRARI F
Revista:
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Editorial:
IEEE COMPUTER SOC
Referencias:
Lugar: Los Alamitos, CA, USA; Año: 2012 p. 706 - 716
ISSN:
1545-5963
Resumen:
In the biological domain, clustering is based on the assumption that genes or metabolites involved in a common biological
process are coexpressed/coaccumulated under the control of the same regulatory network. Thus, a detailed inspection of the grouped
patterns to verify their memberships to well-known metabolic pathways could be very useful for the evaluation of clusters from a
biological perspective. The aim of this work is to propose a novel approach for the comparison of clustering methods over metabolic
data sets, including prior biological knowledge about the relation among elements that constitute the clusters. A way of measuring the
biological significance of clustering solutions is proposed. This is addressed from the perspective of the usefulness of the clusters to
identify those patterns that change in coordination and belong to common pathways of metabolic regulation. The measure summarizes
in a compact way the objective analysis of clustering methods, which respects coherence and clusters distribution. It also evaluates
the biological internal connections of such clusters considering common pathways. The proposed measure was tested in two biological
databases using three clustering methods.