BALZARINI MONICA GRACIELA
Artículos
Título:
Improving cluster visualization in Self-Organizing Maps: Application in Gene Expression Data Analysis
Autor/es:
FERNANDEZ E; BALZARINI M
Revista:
COMPUTERS IN BIOLOGY AND MEDICINE
Referencias:
Lugar: USA; Año: 2007 vol. 37 p. 1677 - 1677
Resumen:
p class="MsoNormal" style="margin: 0pt 47.2pt 0pt 0pt; text-align: justify;">Cluster analysis is one of the crucial steps in the process of Gene Expression Pattern Analysis (GEP). It leads to the discovery or identification of temporal patterns and coexpressed genes. GEP analysis involves highly dimensional and multivariate data which demands appropriate tools. A good alternative for grouping many multidimensional objects is Self-Organizing Maps (SOM), an unsupervised neural network algorithm able to find relationships among data. SOM groups and maps them topologically. However, it may be difficult to identify clusters by means of usual visualization tools for SOM. We propose a simple algorithm to identify and visualize clusters in SOM (the RP-Q method). The RP is a new node-adaptive attribute that moves in a two dimensional virtual space imitating the movement of the codebooks vectors of the SOM net into the input space. The Q statistics evaluates the SOM str