Abstract

Simultaneous overexpression or underexpression of multiple genes, used in various forms as probes in the high-throughput microarray experiments, facilitates the identification of their underlying functional proximity. This kind of functional associativity (or conversely the separability) between the genes can be represented proficiently using co-expression networks. The extensive repository of diversified microarray data encounters a recent problem of multi-experimental data integration for the aforesaid purpose. This paper highlights a novel integration method of gene co-expression networks, based on the search for their consensus network, derived from diverse microarray experimental data for the purpose of clustering. The proposed methodology avoids the bias arising from missing value estimation. The method has been applied on microarray datasets arising from different category of experiments to integrate them. The consensus network, thus produced, reflects robustness based on biological validation.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.