Maximum diversity weighting for biomarkers with application in HIV-1 vaccine studies.
HIV-1 vaccine development
multivariate analysis
profile hidden Markov model
unsupervised feature selection
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
10 09 2019
10 09 2019
Historique:
received:
11
05
2018
revised:
15
02
2019
accepted:
08
05
2019
pubmed:
20
6
2019
medline:
18
12
2020
entrez:
20
6
2019
Statut:
ppublish
Résumé
While studying the association between risk of HIV-1 infection and vaccine-elicited immune responses in preventative HIV-1 vaccine recipients, we encountered a need to combine a collection of biomarkers in an unsupervised fashion with the goal of preserving signal diversity within that collection. Inspired by methods for weighting protein sequences from the biological sequence analysis literature, we propose novel methods for weighting biomarkers, which we call maximum diversity weights. These weights are defined as the weights that maximize measures of signal diversity within a collection of biomarkers. While the optimization problems do not admit analytical solutions, they are convex and hence can be solved efficiently using iterative search algorithms. Through Monte Carlo studies and a real data example from HIV-1 vaccine research, we show that using maximum diversity weights in association studies can lead to an increase in power over other commonly used weights such as uniform weights or principal component-based weights.
Identifiants
pubmed: 31215662
doi: 10.1002/sim.8212
pmc: PMC6684395
mid: NIHMS1041471
doi:
Substances chimiques
AIDS Vaccines
0
Biomarkers
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
3936-3946Subventions
Organisme : NIAID NIH HHS
ID : R01 AI122991
Pays : United States
Organisme : NIH HHS
ID : S10 OD020069
Pays : United States
Organisme : NIAID NIH HHS
ID : UM1 AI068635
Pays : United States
Informations de copyright
© 2019 John Wiley & Sons, Ltd.
Références
N Engl J Med. 2009 Dec 3;361(23):2209-20
pubmed: 19843557
J Mol Biol. 1990 Dec 20;216(4):813-8
pubmed: 2176240
Clin Infect Dis. 2012 Jun;54(11):1615-7
pubmed: 22437237
N Engl J Med. 2012 Apr 5;366(14):1275-86
pubmed: 22475592
Proc Natl Acad Sci U S A. 2013 May 28;110(22):9019-24
pubmed: 23661056
J Comput Biol. 1995 Spring;2(1):9-23
pubmed: 7497123
Proc Int Conf Intell Syst Mol Biol. 1995;3:215-21
pubmed: 7584440
J Mol Biol. 1994 Nov 4;243(4):574-8
pubmed: 7966282
Bioinformatics. 1998;14(9):755-63
pubmed: 9918945