hal-00616640, version 1
Estimating the Class Posterior Probabilities in Protein Secondary Structure Prediction
PRIB 2011 (2011) ???
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CNRS : UMR7503 – INRIA – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL) France
Bibliographic reference
- Type of document: Peer-reviewed conferences/proceedings
- Subject:
Computer Science/Bioinformatics Life Sciences/Quantitative Methods - Title: Estimating the Class Posterior Probabilities in Protein Secondary Structure Prediction
- Abstract: Support vector machines, let them be bi-class or multi-class, have proved efficient for protein secondary structure prediction. They can be used either as sequence-to-structure classifier, structure-to-structure classifier, or both. Compared to the classifier most commonly found in the main prediction methods, the multi-layer perceptron, they exhibit one single drawback: their outputs are not class posterior probability estimates. This paper addresses the problem of post-processing the outputs of multi-class support vector machines used as sequence-to-structure classifiers with a structure-to-structure classifier estimating the class posterior probabilities. The aim of this comparative study is to obtain improved performance with respect to both criteria: prediction accuracy and quality of the estimates.
- Fulltext language: English
- Book title: Actes de PRIB 2011
- Audience: international
- Publication date: 2011-11
- Page, identifiant, ...: ???
- Conference or book title: PRIB 2011
- Conference date: 2011-11
- Country: Netherlands
- Keyword(s): protein secondary structure prediction – multi-class support vector machines – class membership probabilities
- hal-00616640, version 1
- http://hal.archives-ouvertes.fr/hal-00616640
- oai:hal.archives-ouvertes.fr:hal-00616640
- From:
- Submitted on: Tuesday, 23 August 2011 15:38:15
- Updated on: Tuesday, 23 August 2011 15:38:15


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