\"As an important experimental technology, DNA microarray provides an effective way to measure the expression levels of tens of thousands of genes simultaneously under different conditions, which makes it possible to investigate the gene activities of the whole genome. However, computational challenges have to be faced as a result of the large volume of generated data. In this dissertation, two important applications of microarray data, i.e., genetic regulatory networks inference and cancer classification, are addressed with machine learning and neural networks\"--Abstract, page iii.
","abstract_html":"<p>"As an important experimental technology, DNA microarray provides an effective way to measure the expression levels of tens of thousands of genes simultaneously under different conditions, which makes it possible to investigate the gene activities of the whole genome. However, computational challenges have to be faced as a result of the large volume of generated data. In this dissertation, two important applications of microarray data, i.e., genetic regulatory networks inference and cancer classification, are addressed with machine learning and neural networks"--Abstract, page iii.</p>","abstract_has_math":false,"creators":["Xu, Rui"],"institution":"University of Missouri--Rolla","degree_name":"Ph. D. in Electrical Engineering","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-02-10T08:00:00Z","date_published":"2016-02-10T08:00:00Z","updated_at":"2026-07-24T03:20:12Z","subjects":["Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/1696","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Xu, Rui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-02-10T08:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation - Citation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D. in Electrical Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Rolla"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsmine.mst.edu/doctoral_dissertations/1696"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"As an important experimental technology, DNA microarray provides an effective way to measure the expression levels of tens of thousands of genes simultaneously under different conditions, which makes it possible to investigate the gene activities of the whole genome. However, computational challenges have to be faced as a result of the large volume of generated data. In this dissertation, two important applications of microarray data, i.e., genetic regulatory networks inference and cancer classification, are addressed with machine learning and neural networks\"--Abstract, page iii.
"]},{"key":"dc:title","label":"Title","values":["Microarray gene expression data analysis using machine learning and neural networks"]}]}],"canonical_facts":{"dc:creator":["Xu, Rui"],"dc:date.available":["2016-02-10T08:00:00Z"],"dc:description.abstract":["\"As an important experimental technology, DNA microarray provides an effective way to measure the expression levels of tens of thousands of genes simultaneously under different conditions, which makes it possible to investigate the gene activities of the whole genome. However, computational challenges have to be faced as a result of the large volume of generated data. In this dissertation, two important applications of microarray data, i.e., genetic regulatory networks inference and cancer classification, are addressed with machine learning and neural networks\"--Abstract, page iii.
"],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/1696"],"dc:subject":["Electrical and Computer Engineering"],"dc:title":["Microarray gene expression data analysis using machine learning and neural networks"],"dc:type":["Dissertation - Citation"],"thesis:degree_name":["Ph. D. in Electrical Engineering"],"thesis:institution_name":["University of Missouri--Rolla"]},"updated_at":"2026-07-24T03:20:12Z"}