scholarly journals Microarray Data Analysis Toolbox (MDAT): for normalization, adjustment and analysis of gene expression data

2004 ◽  
Vol 20 (18) ◽  
pp. 3687-3690 ◽  
Author(s):  
N. Knowlton ◽  
I. M. Dozmorov ◽  
M. Centola
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Expression Data

scholarly journals Text Mining Perspectives in Microarray Data Mining

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Jeyakumar Natarajan
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Text Mining ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Machine Learning Algorithms ◽  
Microarray Data Analysis ◽  
Expression Data ◽  
Related Data ◽  
Mining Methods

Current microarray data mining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In recent years, there has been a growing interest in methods that attempt to discover patterns based on multiple but related data sources. Gene expression data and the corresponding literature data are one such example. This paper suggests a new approach to microarray data mining as a combination of text mining (TM) and information extraction (IE). TM is concerned with identifying patterns in natural language text and IE is concerned with locating specific entities, relations, and facts in text. The present paper surveys the state of the art of data mining methods for microarray data analysis. We show the limitations of current microarray data mining methods and outline how text mining could address these limitations.

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

2003 ◽  
Vol 01 (03) ◽  
pp. 541-586 ◽  
Author(s):  
Tero Aittokallio ◽  
Markus Kurki ◽  
Olli Nevalainen ◽  
Tuomas Nikula ◽  
Anne West ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Biological Research ◽  
Microarray Experiments ◽  
Dna Microarray Data ◽  
Open Questions ◽  
Analysis Technique ◽  
Wide Range

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

COMBINING GENERALIZED NMF AND DISCRIMINATIVE MIXTURE MODELS FOR CLASSIFICATION OF GENE EXPRESSION DATA

2008 ◽  
Vol 22 (08) ◽  
pp. 1587-1598 ◽  
Author(s):  
WEIXIANG LIU ◽  
KEHONG YUAN ◽  
JIAN WU ◽  
DATIAN YE ◽  
ZHEN JI ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mixture Model ◽  
Gene Expression Data ◽  
Small Sample Size ◽  
Data Classification ◽  
Small Sample ◽  
Training Data ◽  
Microarray Data Analysis ◽  
Expression Data

Classification of gene expression samples is a core task in microarray data analysis. How to reduce thousands of genes and to select a suitable classifier are two key issues for gene expression data classification. This paper introduces a framework on combining both feature extraction and classifier simultaneously. Considering the non-negativity, high dimensionality and small sample size, we apply a discriminative mixture model which is designed for non-negative gene express data classification via non-negative matrix factorization (NMF) for dimension reduction. In order to enhance the sparseness of training data for fast learning of the mixture model, a generalized NMF is also adopted. Experimental results on several real gene expression datasets show that the classification accuracy, stability and decision quality can be significantly improved by using the generalized method, and the proposed method can give better performance than some previous reported results on the same datasets.

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