SOFT COMPUTING FOR DYNAMIX SERIES CLASSIFICATION IN GENE EXPRESSION PROFILING
Our won Soft Computing comprises several algorithms for co-expressed genes grouping in data analysis microarrays (MDA). Suitable for researchers trying to determine relevant genes and their co-expressed relations for large dynamic data sets so that an output feature can be optimised
Some of the included algorithms are:
- Shape Index(SC). Grouping dismissing the output of each sample.
- Output Shape Index (SOC). Grouping according to the gene correlation with the output.
- Dynamic Shape Index (DSC).Dynamic version of SC.
- Output Dynamic Shape Index (DSOC). Dynamic version of SOC.
- Relaxed Shape Index (RSC). SOC method enhancement.
The software includes fusion methods that combine in just one group every grouping created from each of the independently generated temporal series of microarray data. The most relevant clusters detection among the available ones is performed by using several measurements on the genes, such as Information Correlation Coefficient (ICC), Pearson Correlation Coefficient (PCC) and Shape Increase measures.
Example Cluster of genes obtained by the software