Listen to Genes: Dealing with Microarray Data in the Frequency Domain

Listen to Genes: Dealing with Microarray Data in the Frequency Domain

BACKGROUND We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes. METHODOLOGY Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation among error terms across replicates. ...

Diagnosis of Breast Cancer Subtypes using the Selection of Effective Genes from Microarray Data

Introduction: Early diagnosis of breast cancer and the identification of effective genes are important issues in the treatment and survival of the patients. Gene expression data obtained using DNA microarray in combination with machine learning algorithms can provide new and intelligent methods for diagnosis of breast cancer. Methods: Data on the expression of 9216 genes from 84 patients across...

cDNA Microarray Data Normalization

Uniqueness of meromorphic functions dealing with multiple values in an angular domain

This paper uses the Tsuji’s characteristic to investigate the uniqueness of transcen- dental meromorphic function with shared values in an angular domain dealing with the multiple values which improve a result of J. Zheng.

Adaptive thresholds to detect differentially expressed genes in microarray data

To detect changes in gene expression data from microarrays, a fixed threshold for fold difference is used widely. However, it is not always guaranteed that a threshold value which is appropriate for highly expressed genes is suitable for lowly expressed genes. In this study, aiming at detecting truly differentially expressed genes from a wide expression range, we proposed an adaptive threshold ...