Integrated analysis of gene expression and genome-wide DNA methylation for tumor prediction: An association rule mining-based approach

2013 ◽  
Author(s):  
Saurav Mallik ◽  
Anirban Mukhopadhyay ◽  
Ujjwal Maulik ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Association Rule ◽  
Association Rule Mining ◽  
Integrated Analysis ◽  
Rule Mining ◽  
Genome Wide

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.

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 Integrated analysis of DNA methylation and gene expression profiles identified S100A9 as a potential biomarker in ulcerative colitis

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Shasha Su ◽  
Wenjie Kong ◽  
Jing Zhang ◽  
Xinguo Wang ◽  
Hongmei Guo
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Dna Methylation ◽  
Expression Profiles ◽  
Osteoclast Differentiation ◽  
Functional Enrichment ◽  
Functional Network ◽  
Integrated Analysis ◽  
Genome Wide ◽  
Potential Biomarker

Abstract Ulcerative colitis (UC) is a prevalent relapsing-remitting inflammatory bowel disease whose pathogenetic mechanisms remain elusive. In the present study, colonic biopsies samples from three UC patients treated in the Traditional Chinese Medicine Hospital and three healthy controls were obtained. The genome-wide mRNA and lncRNA expression of the samples were profiled through Agilent gene expression microarray. Moreover, the genome-wide DNA methylation dataset of normal and UC colon tissues was also downloaded from GEO for a collaborative analysis. Differential expression of lncRNA (DELs) and mRNAs (DEMs) in UC samples compared with healthy samples were identified by using limma Bioconductor package. Differentially methylated promoters (DMPs) in UC samples compared with controls were obtained through comparing the average methylation level of CpGs located at promoters by using t-test. Functional enrichment analysis was performed by the DAVID. STRING database was applied to the construction of gene functional interaction network. As a result, 2090 DEMs and 1242 DELs were screened out in UC samples that were closely associated with processes related to complement and coagulation cascades, osteoclast differentiation vaccinia, and hemorrhagic diseases. A total of 90 DEMs and 72 DELs were retained for the construction of functional network for the promoters of their corresponding genes were identified as DMPs. S100A9, HECW2, SOD3 and HIX0114733 showed high interaction degrees in the functional network, and expression of S100A9 was confirmed to be significantly elevated in colon tissues of UC patients compared with that of controls by qRT-PCR that was consistent with gene microarray analysis. These indicate that S100A9 could potentially be used as predictive biomarkers in UC.

scholarly journals Integrated analysis of genome-wide DNA methylation and gene expression profiles identifies potential novel biomarkers of rectal cancer

Oncotarget ◽  
2016 ◽  
Vol 7 (38) ◽  
pp. 62547-62558 ◽  
Author(s):  
Jiufeng Wei ◽  
Guodong Li ◽  
Jinning Zhang ◽  
Yuhui Zhou ◽  
Shuwei Dang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Rectal Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Integrated Analysis ◽  
Genome Wide ◽  
Novel Biomarkers
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