NDLI: Mining transcriptional association rules from breast cancer profile data

Content Provider	IEEE Xplore Digital Library
Author	Malpani, R. Meiliu Lu Du Zhang Wing Kin Sung
Copyright Year	2011
Description	Author affiliation: School of Computing, National University of Singapore, Singapore (Wing Kin Sung) \|\| Computer Science Department, California State University, Sacramento, USA (Malpani, R.; Meiliu Lu; Du Zhang)
Abstract	To gain insight into regulatory mechanisms underlying the transcription process of gene expressions, we need to understand the co-expressed gene sets under common regulatory mechanisms. Though computational methods have been developing to identify expression module, challenges still remain for cancer related gene expression profiling. In this paper, we have developed a method of data preprocessing and two different association rule mining approaches for discovering breast cancer regulatory mechanisms of gene module. Our data preprocessing task involved with two independent data sources: (a) a single breast cancer patient profile data file, (b) a candidate enhancer information data file. Using the integrated data, we also conducted four experiments of the association rule mining.
Starting Page	154
Ending Page	159
File Size	360789
Page Count	6
File Format	PDF
ISBN	9781457709647
e-ISBN	9781457709661
e-ISBN	9781457709654
DOI	10.1109/IRI.2011.6009538
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Association rules Breast cancer Gene expression Data preprocessing DNA Machine Learning Data mining Information Integration Association rule mining
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

Identification of temporal association rules from time-series microarray data set: temporal association rules

Unveiling Fuzzy Associations Between Breast Cancer Prognostic Factors and Gene Expression Data

Constructing Regulatory Networks from Gene Expression Data Using Association Rule Mining

Finding Time-Delayed Gene Regulation Patterns from Microarray Data

Scalable Rule-Based Gene Expression Data Classification

Novel Data Mining Techniques in aCGH based Breast Cancer Subtypes Profiling: the Biological Perspective

From cancer gene expression data to simple vital rules

Incrememtal Maintenance of Ontology-Exploiting Association Rules

Analysis of DNA Microarray Data using Association Rules: A Selective Study

Mining transcriptional association rules from breast cancer profile data

Similar Documents

Identification of temporal association rules from time-series microarray data set: temporal association rules

Unveiling Fuzzy Associations Between Breast Cancer Prognostic Factors and Gene Expression Data

Constructing Regulatory Networks from Gene Expression Data Using Association Rule Mining

Finding Time-Delayed Gene Regulation Patterns from Microarray Data

Scalable Rule-Based Gene Expression Data Classification

Novel Data Mining Techniques in aCGH based Breast Cancer Subtypes Profiling: the Biological Perspective

From cancer gene expression data to simple vital rules

Incrememtal Maintenance of Ontology-Exploiting Association Rules

Analysis of DNA Microarray Data using Association Rules: A Selective Study

Mining transcriptional association rules from breast cancer profile data