English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  ccSVM: correcting Support Vector Machines for confounding factors in biological data classification

Li, L., Rakitsch, B., & Borgwardt, K. (2011). ccSVM: correcting Support Vector Machines for confounding factors in biological data classification. Bioinformatics, 27(13), i342-i348.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BAF6-A Version Permalink: http://hdl.handle.net/21.11116/0000-0006-C596-2
Genre: Conference Paper

Files

show Files

Creators

show
hide
 Creators:
Li, L1, Author              
Rakitsch, B1, Author              
Borgwardt, K1, Author              
Affiliations:
1Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497664              

Content

show
hide
Free keywords: -
 Abstract: Motivation: Classifying biological data into different groups is a central task of bioinformatics: for instance, to predict the function of a gene or protein, the disease state of a patient or the phenotype of an individual based on its genotype. Support Vector Machines are a wide spread approach for classifying biological data, due to their high accuracy, their ability to deal with structured data such as strings, and the ease to integrate various types of data. However, it is unclear how to correct for confounding factors such as population structure, age or gender or experimental conditions in Support Vector Machine classification. Results: In this article, we present a Support Vector Machine classifier that can correct the prediction for observed confounding factors. This is achieved by minimizing the statistical dependence between the classifier and the confounding factors. We prove that this formulation can be transformed into a standard Support Vector Machine with rescaled input data. In our experiments, our confounder correcting SVM (ccSVM) improves tumor diagnosis based on samples from different labs, tuberculosis diagnosis in patients of varying age, ethnicity and gender, and phenotype prediction in the presence of population structure and outperforms state-of-the-art methods in terms of prediction accuracy.

Details

show
hide
Language(s):
 Dates: 2011-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btr204
BibTex Citekey: LiRB2011
 Degree: -

Event

show
hide
Title: 19th Annual International Conference on Intelligent Systems for Molecular Biology and 10th European Conference on Computational Biology (ISMB/ECCB 2011)
Place of Event: Wien, Austria
Start-/End Date: 2011-07-17 - 2011-07-19

Legal Case

show

Project information

show

Source 1

show
hide
Title: Bioinformatics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 27 (13) Sequence Number: - Start / End Page: i342 - i348 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991