Abstract

The aim of this paper is to identify the co-expressed potential genes that may serve for the development of the portions of normal or tumor. This paper differentiates the co-expressed genes into normal samples and tumor samples from gene expression dataset GSE25066. Since the dataset has vague boundaries and having common characteristics between the clusters, identifying the subgroups contain similar gene expression is really a tricky task one. Therefore, this paper introduces an effective fuzzy iterative clustering algorithm by incorporating kernel function, possibilistic c-means, fuzzy memberships, neighborhood information, median of neighboring objects and penalty term. The performances of the proposed clustering techniques have been shown through the succession experimental works on GSE25066. The effects of clustering results have been proved through comparing the resulted classes with ground truth.

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.