{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Goyal R"],"funding":["NCATS NIH HHS","NIDDK NIH HHS","NIDA NIH HHS","James B. Pendleton Charitable Trust","NIAID NIH HHS","NIH","Department of Veterans Affairs"],"pagination":["e0291367"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10521982"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(9)"],"pubmed_abstract":["Objective
Identify system-level features in HIV migration within a host across body tissues. Evaluate heterogeneity in the presence and magnitude of these features across hosts.Method
Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the complex dependencies of HIV migration among tissues as a network and model these networks using the family of exponential random graph models (ERGMs). ERGMs allow for the statistical assessment of whether network features occur more (or less) frequently in viral migration than might be expected by chance. The analysis investigates five potential features of the viral migration network: (1) bi-directional flow between tissues; (2) preferential migration among tissues in the same biologi"],"journal":["PloS one"],"pubmed_title":["Identification of system-level features in HIV migration within a host."],"pmcid":["PMC10521982"],"funding_grant_id":["R01 DA055491","P01 AI131385","R01 DK131532","R01 AI147821","P01 AI169609","DP2 DA051915","UL1 TR001442","P30 AI036214","UM1 AI164570","UM1 AI164559","R01 AI147441"],"pubmed_authors":["De Gruttola V","Gianella S","Goyal R","Caballero G","Porrachia M","Chaillon A","Woodworth B","Smith DM","Ignacio C"],"additional_accession":[]},"is_claimable":false,"name":"Identification of system-level features in HIV migration within a host.","description":"Objective
Identify system-level features in HIV migration within a host across body tissues. Evaluate heterogeneity in the presence and magnitude of these features across hosts.Method
Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the complex dependencies of HIV migration among tissues as a network and model these networks using the family of exponential random graph models (ERGMs). ERGMs allow for the statistical assessment of whether network features occur more (or less) frequently in viral migration than might be expected by chance. The analysis investigates five potential features of the viral migration network: (1) bi-directional flow between tissues; (2) preferential migration among tissues in the same biologi","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2026-07-14T23:00:32.315Z","creation":"2026-06-26T03:08:01.842Z"},"accession":"S-EPMC10521982","cross_references":{"pubmed":["37751407"],"doi":["10.1371/journal.pone.0291367"]}}