A design method of DNA chips using hierarchical self-organizing maps

Abstract

In this paper, we introduce a design method of DNA chips using Self Organizing Maps(S0M) and Hierarchical SelfOrganizing Maps(H-SOM). DNA chips are powerful tools for sequencings and SNP(Sing1e Nucleotide Polymorphism) analyses of DNA sequences. A DNA chip is an array of DNA probes which can be hybridized with complement subsequences in the target sequence. However, conventional DNA chips are showing tendency to be comprised of large number of long probes and get large in size to achieve high resolution. To shrink the size of DNA chips, design method is considered to be important. To solve this problem, we applied SOM to extract common features of DNA sequences using proper number of probes which efficiently cover the target sequence with sufficient resolutions. Furthermore, H-SOM can design the chip comprised of long probes more efficiently than SOM can. We have designed some DNA chips from the sequence data in genome database using our SOM and H-SOM algorithm and evaluated the chips by computer simulations of SNP analyses.

Spatial Clustering Using Hierarchical SOM

“…HSOMs are often used in application fields where a structured decomposition into smaller and layered problems is convenient. Some examples include: remote sensing classification [45], image compression [28], ontology [43,50], speech recognition [51] pattern classification and extraction using health data [52][53][54], species data [55], financial data [56], climate data [57],,music data [58,59] and electric power data [60].…”