Work

Fuzzy Types Clustering For Microarray Data

Year: 2007

Type: article

Abstract: The main goal of microarray experiments is to quantify the expression of every object on a slide as precisely as possible, with a further goal of clustering the objects. Recently, many studies have discussed clustering issues involving similar patterns of gene expression. This paper presents an application of fuzzy-type methods for clustering DNA microarray data that can be applied to typical comparisons. Clustering and analyses were performed on microarray and simulated data. The results show that fuzzy-possibility c-means clustering substantially improves the findings obtained by others.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.1059828

Authors: Kim, Seo Young, Choi, Tai Myong

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: Chonnam National University, Institute for Basic Science

OpenAlex ID: W2174835692

FWCI: 0.395

Cites:

Cited by: 20

Related to: 10