Analyzing Alzheimer's disease gene expression dataset using clustering and association rule mining

2014 ◽  
Author(s):  
Benoit Le Queau ◽  
Omair Shafiq ◽  
Reda Alhajj
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Rule ◽  
Association Rule Mining ◽  
Disease Gene ◽  
Gene Expression Dataset ◽  
Rule Mining

AN ASSOCIATION RULE MINING APPROACH FOR CO-REGULATED SIGNATURE GENES IDENTIFICATION IN CANCER

2009 ◽  
Vol 18 (08) ◽  
pp. 1409-1423 ◽  
Author(s):  
K. R. SEEJA ◽  
M. A. ALAM ◽  
S. K. JAIN
Keyword(s):  
Gene Expression ◽  
Association Rule ◽  
Association Rule Mining ◽  
Low Cost ◽  
Published Data ◽  
Cancer Tissue ◽  
Rule Mining ◽  
Signature Genes ◽  
Microarray Chips ◽  
Sage Data

When a normal cell becomes cancerous there will be change in expression of many genes in that cell. Identification of these changes in gene expression in cancer tissue may lead to the development of novel tools for early diagnosis and effective therapeutics. In this paper we present an association rule mining approach to identify the association between the genes that are differentially expressed in cancer tissue compared to normal tissue. We design an association rule mining algorithm GeneExpMiner for gene expression data mining. Serial Analysis of Gene Expression (SAGE) data related to pancreas cancer is used to demonstrate the approach. It is expected that the approach will help in developing better treatment methodologies for cancer and designing low cost microarray chips for diagnosing cancer. The results have been validated in terms of Gene Ontology and the signature genes that we have identified are matching with the published data.

scholarly journals Age and Alzheimer’s disease gene expression profiles reversed by the glutamate modulator riluzole

2016 ◽  
Vol 22 (2) ◽  
pp. 296-305 ◽  
Author(s):  
A C Pereira ◽  
J D Gray ◽  
J F Kogan ◽  
R L Davidson ◽  
T G Rubin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Gene ◽  
Expression Profiles ◽  
Gene Expression Profiles

scholarly journals Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer’s Disease Gene Expression Data

2020 ◽  
Vol 75 (4) ◽  
pp. 1417-1435 ◽  
Author(s):  
Barbara Kramarz ◽  
Rachael P. Huntley ◽  
Milagros Rodríguez-López ◽  
Paola Roncaglia ◽  
Shirin C.C. Saverimuttu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Ontology ◽  
Gene Expression Data ◽  
Disease Gene ◽  
Expression Data

scholarly journals Integrated Statistical and Rule-Mining Techniques for Dna Methylation and Gene Expression Data Analysis

2013 ◽  
Vol 3 (2) ◽  
pp. 101-115 ◽  
Author(s):  
Saurav Mallik ◽  
Anirban Mukhopadhyay ◽  
Ujjwal Maulik
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Association Rule ◽  
Association Rule Mining ◽  
Statistical Tests ◽  
Rank Aggregation ◽  
Majority Voting ◽  
Rule Mining ◽  
Gene Markers ◽  
Weighted Sum Method

Abstract For determination of the relationships among significant gene markers, statistical analysis and association rule mining are considered as very useful protocols. The first protocol identifies the significant differentially expressed/methylated gene markers, whereas the second one produces the interesting relationships among them across different types of samples or conditions. In this article, statistical tests and association rule mining based approaches have been used on gene expression and DNA methylation datasets for the prediction of different classes of samples (viz., Uterine Leiomyoma/class-formersmoker and uterine myometrium/class-neversmoker). A novel rule-based classifier is proposed for this purpose. Depending on sixteen different rule-interestingness measures, we have utilized a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training set of data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to estimate its class-label through weighted-sum method. We have run this classifier on the combined dataset using 4-fold cross-validations, and thereafter a comparative performance analysis has been made with other popular rulebased classifiers. Finally, the status of some important gene markers has been identified through the frequency analysis in the evolved rules for the two class-labels individually to formulate the interesting associations among them.

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