Fusion: a Visualization Framework for Interactive Ilp Rule Mining With Applications to Bioinformatics

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dc.contributor.authorIndukuri, Kiran Kumaren
dc.contributor.committeechairNorth, Christopher L.en
dc.contributor.committeememberHeath, Lenwood S.en
dc.contributor.committeememberGrene, Ruthen
dc.contributor.committeememberRamakrishnan, Narenen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2014-03-14T20:50:29Zen
dc.date.adate2005-01-04en
dc.date.available2014-03-14T20:50:29Zen
dc.date.issued2004-12-01en
dc.date.rdate2005-01-04en
dc.date.sdate2004-12-20en
dc.description.abstractMicroarrays provide biologists an opportunity to find the expression profiles of thousands of genes simultaneously. Biologists try to understand the mechanisms underlying the life processes by finding out relationships between gene-expression and their functional categories. Fusion is a software system that aids the biologists in performing microarray data analysis by providing them with both visual data exploration and data mining capabilities. Its multiple view visual framework allows the user to choose different views for different types of data. Fusion uses Proteus, an Inductive Logic Programming (ILP) rule finding algorithm to mine relationships in the microarray data. Fusion allows the user to explore the data interactively, choose biases, run the data mining algorithms and visualize the discovered rules. Fusion has the capability to smoothly switch across interactive data exploration and batch data mining modes. This optimizes the knowledge discovery process by facilitating a synergy between the interactivity and usability of visualization process with the pattern-finding abilities of ILP rule mining algorithms. Fusion was successful in helping biologists better understand the mechanisms underlying the acclimatization of certain varieties of Arabidopsis to ozone exposure.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-12202004-140019en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12202004-140019/en
dc.identifier.urihttp://hdl.handle.net/10919/36326en
dc.publisherVirginia Techen
dc.relation.haspartThesis.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectFusionen
dc.subjectILP Rule Miningen
dc.subjectVisualizationen
dc.subjectMicroarraysen
dc.subjectBioinformaticsen
dc.titleFusion: a Visualization Framework for Interactive Ilp Rule Mining With Applications to Bioinformaticsen
dc.typeThesisen
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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