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Identifikace separujících vlastností molekulárních fragmentů pomocí strojového učení
dc.contributor.advisorHoksza, David
dc.creatorRavi, Aakash
dc.date.accessioned2017-06-02T01:00:16Z
dc.date.available2017-06-02T01:00:16Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/20.500.11956/2093
dc.description.abstractChosen molecular representation is one of the key parameters of virtual screening campaigns where one is searching in-silico for active molecules with respect to given macromolecular target. Most campaigns employ a molecular representation in which a molecule is represented by the presence or absence of a predefined set of topological fragments. Often, this information is enriched by physiochemical features of these fragments: i.e. the representation distinguishes fragments with identical topology, but different features. Given molecular representation, however, most approaches always use the same static set of features irrespective of the specific target. The goal of this thesis is, given a set of known active and inactive molecules with respect to a target, to study the possibilities of parameterization of a fragment-based molecular representation with feature weights dependent on the given target. In this setting, we are given a very general molecular representation, with targets represented by sets of known active and inactive molecules. We subsequently propose a machine-learning approach that would identify which of the features are relevant for the given target. This will be done using a multi-stage pipeline that includes data preprocessing using statistical imputation and dimensionality...en_US
dc.languageEnglishcs_CZ
dc.language.isoen_US
dc.publisherUniverzita Karlova, Matematicko-fyzikální fakultacs_CZ
dc.subjectcheminformatikacs_CZ
dc.subjectstrojové učenícs_CZ
dc.subjectmolekulární reprezentacecs_CZ
dc.subjectcheminformaticsen_US
dc.subjectmachine learningen_US
dc.subjectmolecular representationen_US
dc.titleMachine learning-based identification of separating features in molecular fragmentsen_US
dc.typebakalářská prácecs_CZ
dcterms.created2017
dcterms.dateAccepted2017-01-31
dc.description.departmentDepartment of Software Engineeringen_US
dc.description.departmentKatedra softwarového inženýrstvícs_CZ
dc.description.facultyMatematicko-fyzikální fakultacs_CZ
dc.description.facultyFaculty of Mathematics and Physicsen_US
dc.identifier.repId172518
dc.title.translatedIdentifikace separujících vlastností molekulárních fragmentů pomocí strojového učenícs_CZ
dc.contributor.refereeŠkoda, Petr
dc.identifier.aleph002126001
thesis.degree.nameBc.
thesis.degree.levelbakalářskécs_CZ
thesis.degree.disciplineObecná informatikacs_CZ
thesis.degree.disciplineGeneral Computer Scienceen_US
thesis.degree.programInformatikacs_CZ
thesis.degree.programComputer Scienceen_US
uk.thesis.typebakalářská prácecs_CZ
uk.taxonomy.organization-csMatematicko-fyzikální fakulta::Katedra softwarového inženýrstvícs_CZ
uk.taxonomy.organization-enFaculty of Mathematics and Physics::Department of Software Engineeringen_US
uk.faculty-name.csMatematicko-fyzikální fakultacs_CZ
uk.faculty-name.enFaculty of Mathematics and Physicsen_US
uk.faculty-abbr.csMFFcs_CZ
uk.degree-discipline.csObecná informatikacs_CZ
uk.degree-discipline.enGeneral Computer Scienceen_US
uk.degree-program.csInformatikacs_CZ
uk.degree-program.enComputer Scienceen_US
thesis.grade.csVýborněcs_CZ
thesis.grade.enExcellenten_US
uk.abstract.enChosen molecular representation is one of the key parameters of virtual screening campaigns where one is searching in-silico for active molecules with respect to given macromolecular target. Most campaigns employ a molecular representation in which a molecule is represented by the presence or absence of a predefined set of topological fragments. Often, this information is enriched by physiochemical features of these fragments: i.e. the representation distinguishes fragments with identical topology, but different features. Given molecular representation, however, most approaches always use the same static set of features irrespective of the specific target. The goal of this thesis is, given a set of known active and inactive molecules with respect to a target, to study the possibilities of parameterization of a fragment-based molecular representation with feature weights dependent on the given target. In this setting, we are given a very general molecular representation, with targets represented by sets of known active and inactive molecules. We subsequently propose a machine-learning approach that would identify which of the features are relevant for the given target. This will be done using a multi-stage pipeline that includes data preprocessing using statistical imputation and dimensionality...en_US
uk.file-availabilityV
uk.publication.placePrahacs_CZ
uk.grantorUniverzita Karlova, Matematicko-fyzikální fakulta, Katedra softwarového inženýrstvícs_CZ
dc.identifier.lisID990021260010106986


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