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  Estimating and Using the Uncertainty in the Phenotypic Resistance Assay of the Hepatitis B Virus to Improve IC-50 Prediction from Genotype

Reuter, K. (2011). Estimating and Using the Uncertainty in the Phenotypic Resistance Assay of the Hepatitis B Virus to Improve IC-50 Prediction from Genotype. Bachelor Thesis, Universität des Saarlandes, Saarbrücken.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-132F-8 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-A7F6-B
Genre: Thesis

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 Creators:
Reuter, Kerstin1, Author           
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              

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 Abstract: Hepatitis B is one of the most frequent infectious diseases worldwide. Thus improvement of therapy is an active area of research. Since the hepatitis B virus can adapt to treatment in terms of resistance mutations, therapeutic approaches often fail. With computational support and statistical learning methods, it is possible to adjust therapy according to the underlying virus. In consequence, an improvement of therapy is enabled due to individual treatment. In this thesis, an approach to predict the resistance from genome sequence is presented. Resistance is defined in terms of viral IC-50 concentration. A special feature described is the usage of sample specific weights based on the precision of the phenotypic assay. These weights are meant to overcome a distortion of prediction results caused by experimental measurement errors. For this purpose, the LASSO and linear support vector machines were trained with 367 hepatitis B clones treated with four approved antiviral drugs. Using uncertainty estimates, it was possible to moderately improve prediction of the IC50 value from genotype for two out of four drugs. In summary, this thesis provides a recent approach to support individual treatment by improving prediction of the IC-50 value from viral genotype using sample specific weights.

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Language(s): eng - English
 Dates: 2012-03-2020112011
 Publication Status: Issued
 Pages: -
 Publishing info: Saarbrücken : Universität des Saarlandes
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 618837
Other: Local-ID: C125673F004B2D7B-E9F781C21284C83AC12579A3004CAC1A-Reuter2011
 Degree: Bachelor

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