Conformational B-Cell epitope prediction

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dc.contributor.authorStröm, Fredrik
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:23:30Z
dc.date.available2019-07-03T14:23:30Z
dc.date.issued2016
dc.description.abstractThere is demand for higher quality therapeutic proteins in our society. Medical drug developing companies strive for lower development cost and shorter development time. This require faster and more reliable ways of testing the therapeutic proteins before releasing them to the public. This project aimed to investigate one part of this problem, the conformational B-cell epitopes which is the interface between an foreign molecule (antigen) and an antibody. It was done by development of two different epitope models, which then was used as a base for creation of two training data sets. These training sets were then used to train machine learning algorithms in order to classify areas on molecule surfaces which are prone to be an epitope. Different problems in this research area is discussed and possible solutions is proposed.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/245105
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectInformations- och kommunikationsteknik
dc.subjectData- och informationsvetenskap
dc.subjectInformation & Communication Technology
dc.subjectComputer and Information Science
dc.titleConformational B-Cell epitope prediction
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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