S26.2: Using Text Mining Networks for the Context Specific Interpretation of Gene Expression Data

2004 ◽  
Vol 46 (S1) ◽  
pp. 56-56
Author(s):  
Christian Gieger ◽  
Daniel Hanisch ◽  
Juliane Fluck ◽  
Heinz-Theodor Mevissen ◽  
Achim Tresch ◽  
...  
Keyword(s):  
Gene Expression ◽  
Text Mining ◽  
Gene Expression Data ◽  
Expression Data ◽  
Specific Interpretation ◽  
Context Specific

On the limits of active module identification

2021 ◽  
Author(s):  
Olga Lazareva ◽  
Jan Baumbach ◽  
Markus List ◽  
David B Blumenthal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Diameter ◽  
Extensive Study ◽  
Biological Knowledge ◽  
Expression Data ◽  
Module Identification ◽  
Ppi Networks ◽  
Novel Algorithms ◽  
Context Specific

Abstract In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

scholarly journals Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Kristina M Hettne ◽  
André Boorsma ◽  
Dorien A M van Dartel ◽  
Jelle J Goeman ◽  
Esther de Jong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Text Mining ◽  
Gene Expression Data ◽  
Specific Gene ◽  
Expression Data ◽  
Next Generation ◽  
Gene Sets ◽  
Chemical Response

CONTEXT-SPECIFIC GENE REGULATIONS IN CANCER GENE EXPRESSION DATA

2008 ◽  
Author(s):  
INA SEN ◽  
MICHAEL P. VERDICCHIO ◽  
SUNGWON JUNG ◽  
ROBERT TREVINO ◽  
MICHAEL BITTNER ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Specific Gene ◽  
Cancer Gene ◽  
Expression Data ◽  
Context Specific ◽  
Gene Regulations

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.

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