NDLI: Methodological approach from the Best Overall Team in the sbv IMPROVER Diagnostic Signature Challenge

Please wait, while we are loading the content...

Methodological approach from the Best Overall Team in the sbv IMPROVER Diagnostic Signature Challenge

Content Provider	Scilit
Author	Tarca, Adi L. Than, Nandor Gabor Romero, Roberto
Copyright Year	2013
Description	The sbv IMPROVER Diagnostic Signature Challenge used crowdsourcing to identify the best methods to classify clinical samples using transcriptomics data. Participating teams used public microarray data sets to develop prediction models in four disease areas, and then made predictions on blinded test data generated by the organizers. Here we describe the approach of the team for the Perinatology Research Branch (Team PRB; AL Tarca, R Romero), that was awarded the best performing entrant prize out of 54 entrants. The key elements of our approach included: (1) selection of training data sets by trial and error; (2) removal of batch effects by pre-processing the test and training data together; (3) the use of statistical significance and magnitude of change to select biomarkers; and (4) optimization of the number of biomarkers via the cross-validated performance of a simple linear discriminant analysis (LDA) model. Not only were our resulting models ranked consistently high, but they also generated parsimonious signatures of as low as two genes, unlike most of the other top-ranked teams that used hundreds of genes for prediction.
Related Links	https://www.tandfonline.com/doi/pdf/10.4161/sysb.25980?needAccess=true
Ending Page	227
Page Count	11
Starting Page	217
ISSN	21628130
e-ISSN	21628149
DOI	10.4161/sysb.25980
Journal	Systems Biomedicine
Issue Number	4
Volume Number	1
Language	English
Publisher	Informa UK Limited
Publisher Date	2013-10-22
Access Restriction	Open
Subject Keyword	Mathematical and Computational Biology Improver Diagnostic Methodological Approach Sbv Improver Diagnostic Signature Signature Challenge
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

sbv IMPROVER Diagnostic Signature Challenge

Species translatable blood gene signature as a marker of exposure to smoking: Computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology Challenge

sbv IMPROVER The Network Verification Challenge

The sbv IMPROVER Systems Toxicology computational challenge: Identification of human and species-independent blood response markers as predictors of smoking exposure and cessation status

Human blood gene signature as a marker for smoking exposure: Computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge

Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge

Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge

Crowdsourced benchmarking of taxonomic metagenome profilers: lessons learned from the sbv IMPROVER Microbiomics challenge

Methodological approach from the Best Overall Team in the sbv IMPROVER Diagnostic Signature Challenge

Similar Documents

sbv IMPROVER Diagnostic Signature Challenge

Species translatable blood gene signature as a marker of exposure to smoking: Computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology Challenge

sbv IMPROVER The Network Verification Challenge

The sbv IMPROVER Systems Toxicology computational challenge: Identification of human and species-independent blood response markers as predictors of smoking exposure and cessation status

Human blood gene signature as a marker for smoking exposure: Computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge

Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge

Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge

Crowdsourced benchmarking of taxonomic metagenome profilers: lessons learned from the sbv IMPROVER Microbiomics challenge

Methodological approach from the Best Overall Team in the sbv IMPROVER Diagnostic Signature Challenge