High-throughput multi-dimensional scaling (HiT-MDS) for cDNA-array expression data

Autor(en): Strickert, M
Teichmann, S
Sreenivasulu, N
Seiffert, U
Herausgeber: Duch, W
Kacprzyk, J
Zadrozny, S
Stichwörter: clustering; Computer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering; Engineering, Biomedical; gene expression analysis; multi-dimensional scaling; Neurosciences; Neurosciences & Neurology
Erscheinungsdatum: 2005
Herausgeber: SPRINGER-VERLAG BERLIN
Journal: ARTIFICIAL NEURAL NETWORKS: BIOLOGICAL INSPIRATIONS - ICANN 2005, PT 1, PROCEEDINGS
Lecture Notes in Computer Science
Volumen: 3696
Startseite: 625
Seitenende: 633
Zusammenfassung: 
Multidimensional Scaling (MDS) is a powerful dimension reduction technique for embedding high-dimensional data into a low-dimensional target space. Thereby, the distance relationships in the source are reconstructed in the target space as best as possible according to a given embedding criterion. Here, a new stress function with intuitive properties and a very good convergence behavior is presented. Optimization is combined with an efficient implementation for calculating dynamic distance matrix correlations, and the implementation can be transferred to other related algorithms. The suitability of the proposed MDS for high-throughput data (HiT-MDS) is studied in applications to macroarray analysis for up to 12,000 genes.
Beschreibung: 
15th International Conference on Artificial Neural Networks (ICANN 2005), Warsaw, POLAND, SEP 11-15, 2005
ISBN: 9783540287520
ISSN: 03029743

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