NDLI: S-score: A novel scoring method of gene signatures for molecular classification

Content Provider	IEEE Xplore Digital Library
Author	Chen, H.H. Tzu-Hung Hsiao Yidong Chen Keller, C.
Copyright Year	2011
Description	Author affiliation: Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, USA (Chen, H.H.; Tzu-Hung Hsiao; Yidong Chen) \|\| Pediatric Cancer Biology Program, Pap Papé Family Pediatric Research Institute Family, Pediatric Research Institute, Department of Pediatrics, Oregon Health and Science University, Portland, USA (Keller, C.)
Abstract	Gene signatures have been utilized to discover the inferred biological functions under gene expression profiles. We present a novel sample scoring method called Signature-score (S-score). S-score quantifies the expression pattern by using gene signatures and has better accuracy and robustness than other scoring methods. A confidence boundary of S-score is determined to identify the status of samples for which gene signatures represent. Through the quantitative and qualitative analyses of S-score, we will be able to unambiguously identify the qualitative status of enriched pathways or biological processes. This scoring method will not only allow researchers to explore novel functions for microarray studies, but also to focus on known biological functions that manifest the phenotype.
Starting Page	154
Ending Page	157
File Size	648822
Page Count	4
File Format	PDF
ISBN	9781467304917
ISSN	21503001
e-ISBN	9781467304900
e-ISBN	9781467304894
DOI	10.1109/GENSiPS.2011.6169468
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation gene signature Noise Muscles Robustness Gene expression classification pathway analysis Tumors Cancer
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

A novel and efficient method for differential pathway identification

Gene-expression signatures can distinguish gastric cancer grades and stages (2011).

Building Decision Rules Using a Novel Data Driven Method for Microarray Data Classification

Topology-based cancer classification and related pathway mining using microarray data (2006)

Bayesian modelling for biological pathway annotation of gene expression pathway signatures (2010)

Multiclass microarray gene expression classification based on fusion of correlation features

Can systems biology understand pathway activation? Gene expression signatures as surrogate markers for understanding the complexity of pathway activation (2008)

Gene co-expression network analysis of two ovarian cancer datasets

Comparison of discrimination methods for the classification of tumors using gene expression data (2000).

S-score: A novel scoring method of gene signatures for molecular classification

Similar Documents

A novel and efficient method for differential pathway identification

Gene-expression signatures can distinguish gastric cancer grades and stages (2011).

Building Decision Rules Using a Novel Data Driven Method for Microarray Data Classification

Topology-based cancer classification and related pathway mining using microarray data (2006)

Bayesian modelling for biological pathway annotation of gene expression pathway signatures (2010)

Multiclass microarray gene expression classification based on fusion of correlation features

Can systems biology understand pathway activation? Gene expression signatures as surrogate markers for understanding the complexity of pathway activation (2008)

Gene co-expression network analysis of two ovarian cancer datasets

Comparison of discrimination methods for the classification of tumors using gene expression data (2000).

S-score: A novel scoring method of gene signatures for molecular classification