A Comparative Study of Cancer Classification Methods Using Microarray Gene Expression Profile

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.

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Multiclass cancer diagnosis in microarray gene expression profile using mutual information and Support Vector Machine

Intelligent Data Analysis ◽

10.3233/ida-150203 ◽

2016 ◽

Vol 20 (6) ◽

pp. 1425-1439 ◽

Cited By ~ 2

Author(s):

C. Devi Arockia Vanitha ◽

D. Devaraj ◽

M. Venkatesulu

Keyword(s):

Gene Expression ◽

Support Vector Machine ◽

Mutual Information ◽

Gene Expression Profile ◽

Expression Profile ◽

Cancer Diagnosis ◽

Support Vector ◽

Microarray Gene Expression ◽

Microarray Gene Expression Profile ◽

Microarray Gene

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SPECTRAL CLUSTERING ON GENE EXPRESSION PROFILE TO IDENTIFY CANCER TYPES OR SUBTYPES

Jurnal Teknologi ◽

10.11113/jt.v76.4036 ◽

2015 ◽

Vol 76 (1) ◽

Author(s):

Ang Jun Chin ◽

Andri Mirzal ◽

Habibollah Haron

Keyword(s):

Gene Expression ◽

Gene Expression Profile ◽

Expression Profile ◽

Microarray Data ◽

Spectral Clustering ◽

Data Sets ◽

Clustering Methods ◽

Microarray Gene Expression ◽

Cancer Types ◽

Microarray Gene

Gene expression profile is eminent for its broad applications and achievements in disease discovery and analysis, especially in cancer research. Spectral clustering is robust to irrelevant features which are appropriated for gene expression analysis. However, previous works show that performance comparison with other clustering methods is limited and only a few microarray data sets were analyzed in each study. In this study, we demonstrate the use of spectral clustering in identifying cancer types or subtypes from microarray gene expression profiling. Spectral clustering was applied to eleven microarray data sets and its clustering performances were compared with the results in the literature. Based on the result, overall the spectral clustering slightly outperformed the corresponding results in the literature. The spectral clustering can also offer more stable clustering performances as it has smaller standard deviation value. Moreover, out of eleven data sets the spectral clustering outperformed the corresponding methods in the literature for six data sets. So, it can be stated that the spectral clustering is a promising method in identifying the cancer types or subtypes for microarray gene expression data sets.

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A Comprehensive Analysis of the Microarray Gene Expression Protocol and Results Using the Mouse T-cell Gene Expression Profile

Asian Journal of Biochemistry, Genetics and Molecular Biology ◽

10.9734/ajbgmb/2019/v2i430069 ◽

2019 ◽

pp. 1-15

Author(s):

Onoriode Oyiborhoro ◽

Oriakhi Kelly ◽

Esosa S. Uhunmwangho ◽

Kingsley A. Iteire ◽

Enoh F. Akpojotor

Keyword(s):

Gene Expression ◽

T Cell ◽

Gene Expression Profile ◽

Expression Profile ◽

Gene Interaction ◽

Response To Therapy ◽

Microarray Gene Expression ◽

Cell Gene Expression ◽

Microarray Gene ◽

Cell Gene

The microarray technology is a very powerful technology that combines molecular biology and computer technology to analyze the gene expression levels for most or all of the genes in a whole genome simultaneously, at very high resolutions. This technology has wide applications, including gene interaction studies for discovery of genes responsible for different diseases; classification of cancers and other diseases; prediction of clinical outcomes or prognosis for different diseases; response to therapy and development of new therapeutic agents, including gene therapy. It is therefore a very potent, unbiased and sensitive technology for the discovery of novel genes involved in the pathogenesis or control of diseases including cancers and autoimmune diseases. In the present study, we seek to give a clear and detailed account of the microarray gene expression protocol using the mouse T-cell gene expression profile, including challenges involved and how to overcome them, as well as detailed analysis of results obtained.

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