000000195 001__ 195
000000195 005__ 20250424232507.0
000000195 0247_ $$2DOI$$a10.6083/M4SJ1HHS
000000195 037__ $$aETD
000000195 245__ $$aDeveloping and validating a tool for microarray cluster analysis
000000195 260__ $$bOregon Health and Science University
000000195 269__ $$a2004
000000195 336__ $$aThesis
000000195 502__ $$bM.S.
000000195 520__ $$aMicroarray experiments offer the user the potential to monitor gene expression across thousands of genes at once.  However, researchers are often left with a dimensionality problem - too few technological and biological replicates, and thousands of genes to monitor for differential expression. The problem of finding interesting and novel genes within the thousands of genes on a microarray can seem akin to looking for a needle in a haystack of needles.  One highly popular approach to finding interesting genes for further study is finding similar patterns of expression within the data.  A common hypothesis-generating approach, clustering, has been shown to have much potential in finding genes with similar functions.  However, there are a variety of clustering methods, and each has different strengths and weaknesses in finding patterns within microarray data.
000000195 540__ $$fCC BY
000000195 542__ $$fIn copyright - single owner
000000195 650__ $$aCluster Analysis$$028809
000000195 650__ $$aMicroarray Analysis$$035790
000000195 650__ $$aGene Expression$$028717
000000195 691__ $$aSchool of Medicine$$041369
000000195 692__ $$aDepartment of Medical Informatics and Clinical Epidemiology$$041422
000000195 7001_ $$aLaderas, Ted$$uOregon Health and Science University$$041354
000000195 8564_ $$9bda6cb9e-09e7-4875-be0c-d57834ce28e5$$s2287305$$uhttps://digitalcollections.ohsu.edu/record/195/files/195_etd.pdf$$ePublic$$25eefd679959fc649e0995c3e4f268ad1$$31
000000195 905__ $$a/rest/prod/76/53/71/33/76537133j
000000195 909CO $$ooai:digitalcollections.ohsu.edu:195$$pstudent-work
000000195 980__ $$aBiomedical Informatics