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Soft computing for dynamic series classification in gene expression profiling

Country of Origin: Spain
Reference Number: TOES20171107005
Publication Date: 7 November 2017

Summary

A Spanish technology centre has developed and protected a new soft computing solution that comprises several algorithms for co-expressed genes grouping in data analysis microarrays (MDA). 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. R&D institutions and companies are sought for commercial with technical assistance, or license agreement to test the experimental software.

Description

The identification of coexpressed genes from microarray data is a challenging problem in bioinformatics and computational biology. The technological center has developed this experimental software based on previous research work: shape-based clustering models were developed using the pattern of gene expression values over time and further incorporating knowledge about the correlation between the change in the gene expression level and the output value. 

Consequently, the centre has developed a new soft computing system that comprises several algorithms for co-expressed genes grouping in data analysis microarrays (MDA). 

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.

The software is 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. This new soft computing solution has proven to be useful for experimenting with time-series microarray of bacteria.

The technology centre is seeking for R&D groups and companies, working in the industrial microbiology and biotechnology areas and in the field of bioinformatics, for commercial and license agreements.

 

Example Cluster of genes obtained by the software.

Advantages and Innovations

The obtained results of the first testing activities confirmed the existence of relationships between output variables and gene expressions.

Moreover, the shape-based clustering methods showed promising results, being able to guide metabolic engineering actions with the identification of potential targets. As a result of this own research project, a specific experimental software was developed and recently protected.

The use of this new soft computing solution allows a shorter time for the development of new drugs.

Stage Of Development

Available for demonstration

Requested partner

The center is looking for:

-R&D groups and companies working in the industrial microbiology and biotechnology areas, for testing activities and commercial agreements with technical assistance (help desk). 

-Companies in the field of bioinformatics to better bring this software to market, through license agreements.