<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Gerns Storey HL</submitter><funding>NICHD NIH HHS</funding><funding>NCRR NIH HHS</funding><funding>NIAID NIH HHS</funding><pagination>37-44</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC3931433</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>30(1)</volume><pubmed_abstract>The role of HIV-1-specific antibody responses in HIV disease progression is complex and would benefit from analysis techniques that examine clusterings of responses. Protein microarray platforms facilitate the simultaneous evaluation of numerous protein-specific antibody responses, though excessive data are cumbersome in analyses. Principal components analysis (PCA) reduces data dimensionality by generating fewer composite variables that maximally account for variance in a dataset. To identify clusters of antibody responses involved in disease control, we investigated the association of HIV-1-specific antibody responses by protein microarray, and assessed their association with disease progression using PCA in a nested cohort design. Associations observed among collections of antibody resp</pubmed_abstract><journal>AIDS research and human retroviruses</journal><pubmed_title>Use of principal components analysis and protein microarray to explore the association of HIV-1-specific IgG responses with disease progression.</pubmed_title><pmcid>PMC3931433</pmcid><funding_grant_id>K24 HD054314</funding_grant_id><funding_grant_id>5U01AI078213-05</funding_grant_id><funding_grant_id>TL1 RR025016</funding_grant_id><pubmed_authors>Walson JL</pubmed_authors><pubmed_authors>Camerini D</pubmed_authors><pubmed_authors>Singa B</pubmed_authors><pubmed_authors>Naulikha J</pubmed_authors><pubmed_authors>Felgner PL</pubmed_authors><pubmed_authors>Horton H</pubmed_authors><pubmed_authors>Diaz-Ochoa VE</pubmed_authors><pubmed_authors>John-Stewart G</pubmed_authors><pubmed_authors>Gerns Storey HL</pubmed_authors><pubmed_authors>Prindle VC</pubmed_authors><pubmed_authors>Richardson BA</pubmed_authors></additional><is_claimable>false</is_claimable><name>Use of principal components analysis and protein microarray to explore the association of HIV-1-specific IgG responses with disease progression.</name><description>The role of HIV-1-specific antibody responses in HIV disease progression is complex and would benefit from analysis techniques that examine clusterings of responses. Protein microarray platforms facilitate the simultaneous evaluation of numerous protein-specific antibody responses, though excessive data are cumbersome in analyses. Principal components analysis (PCA) reduces data dimensionality by generating fewer composite variables that maximally account for variance in a dataset. To identify clusters of antibody responses involved in disease control, we investigated the association of HIV-1-specific antibody responses by protein microarray, and assessed their association with disease progression using PCA in a nested cohort design. Associations observed among collections of antibody resp</description><dates><release>2014-01-01T00:00:00Z</release><publication>2014 Jan</publication><modification>2026-04-07T14:00:19.327Z</modification><creation>2019-03-27T01:22:13Z</creation></dates><accession>S-EPMC3931433</accession><cross_references><pubmed>24134221</pubmed><doi>10.1089/AID.2013.0088</doi><doi>10.1089/aid.2013.0088</doi></cross_references></HashMap>