Analysis of molecular motion using non-linear dimensionality reduction

dc.contributor.advisorKavraki, Lydia E.en_US
dc.creatorStamati, Hernan F.en_US
dc.date.accessioned2009-06-03T21:06:16Zen_US
dc.date.available2009-06-03T21:06:16Zen_US
dc.date.issued2007en_US
dc.description.abstractUnderstanding the main stable shapes and transitions of biomolecules is key to solving problems in computational biology. Because simulated molecular samples are high-dimensional, it is important to classify them using few parameters. Traditionally, this requires empirical reaction coordinates to be devised by an expert. This work, in contrast, automates the classification by applying an algorithmic tool for non-linear dimensionality reduction, called ScIMAP, that requires minimal user intervention. A comparison with the most popular linear dimensionality reduction technique, Principal Components Analysis, shows how non-linearity is crucial for capturing the main motion parameters. The contribution is validated by several results on increasingly complex systems, ranging from the motion of small peptides to the folding of large proteins. In all cases considered in this work, only 1--3 parameters are sufficient to characterize the motion landscape and prove as excellent reaction coordinates.en_US
dc.format.extent110 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoTHESIS COMP. SCI. 2007 STAMATIen_US
dc.identifier.citationStamati, Hernan F.. "Analysis of molecular motion using non-linear dimensionality reduction." (2007) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/20541">https://hdl.handle.net/1911/20541</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/20541en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectComputer scienceen_US
dc.titleAnalysis of molecular motion using non-linear dimensionality reductionen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Artsen_US
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