NDLI: Statistical methods for joint data mining of gene expression and DNA sequence database

Content Provider	ACM Digital Library
Author	Curran, Marla D. Long, Fan Ge, Nanxiang Liu, Hong
Abstract	One of the purposes of microarray gene expression experiments is to identify genes regulated under specific cellular conditions. With the availability of putative transcription factor binding motifs, it is now possible to relate gene expression pattern to the pattern of transcription factor binding sites (TFBS), as well as study how TFBS interact with each other to control gene expression. The objectives of this study are to develop a systematic approach for combining data from microarray gene expression experiments and the corresponding regulatory motif patterns in order to delineate gene regulation mechanisms. A secondary goal is to develop a predictive model for finding similarly regulated genes. Three consecutive procedures are proposed for such data mining activities. First, a linear mixed-effect model is fit to data from microarray gene expression experiments and potential regulated (positive) genes are identified based on a specific biological hypothesis. Putative TFBS are then retrieved for the identified positive genes and randomly selected controls. Second, a cluster analysis is conducted to reduce collinearity among the binding sites. In the third step, logistic regression is applied to choose the best model to predict gene type (positive, control) based on the numerous TFBS predictors. The above approach was applied to an internal example and a model was developed to predict up-regulated genes in activated T-helper (Th) cells. Using a leave-one-out cross- validation scheme, the model has an 18.9% false positive rate and a 41.7% false negative rate.
Starting Page	122
Ending Page	129
Page Count	8
File Format	PDF
ISSN	19310145 19310153
DOI	10.1145/980972.980987
Journal	ACM SIGKDD Explorations Newsletter (SKDD)
Volume Number	5
Issue Number	2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2000-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cluster analysis Logistic regression T-helper cells Regulatory motifs Transcription factor binding site (tfbs) Microarray Modeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Statistical methods for joint data mining of gene expression and dna sequence database.

Transcription Factor Binding Site Prediction with Multivariate Gene Expression Data (2007)

Transcription factor binding site prediction with multivariate gene expression data (2007).

Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification.

Mining yeast transcriptional regulatory modules from factor dna-binding sites and gene expression data (2004).

Identification of transcription factor binding site motifs on dna by means of statistical physics (2005).

Transcription factor–DNA binding: beyond binding site motifs

Siteseer: Visualisation and Analysis of Transcription Factor Binding Sites in Nucleotide Sequences (2003)

Regulatory Elements in Low-Methylated Regions Predict Directional Change of Gene Expression

Statistical methods for joint data mining of gene expression and DNA sequence database

Similar Documents

Statistical methods for joint data mining of gene expression and dna sequence database.

Transcription Factor Binding Site Prediction with Multivariate Gene Expression Data (2007)

Transcription factor binding site prediction with multivariate gene expression data (2007).

Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification.

Mining yeast transcriptional regulatory modules from factor dna-binding sites and gene expression data (2004).

Identification of transcription factor binding site motifs on dna by means of statistical physics (2005).

Transcription factor–DNA binding: beyond binding site motifs

Siteseer: Visualisation and Analysis of Transcription Factor Binding Sites in Nucleotide Sequences (2003)

Regulatory Elements in Low-Methylated Regions Predict Directional Change of Gene Expression

Statistical methods for joint data mining of gene expression and DNA sequence database