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Vaccine design via nonnegative lasso-based variable selection.

ABSTRACT: There are many different strains of malaria parasites, each represented by a unique sequence of amino acids. A desirable vaccine would match the amino acid sequence of the parasite antigen. Because of the three-dimensional structure of protein, not all sites in the amino acid sequence participate in the binding between the vaccine-induced antibody and the parasite antigen. Nor do all sites have equal importance. In this work, we apply a nonnegative lasso-based variable selection to identify the 'important' amino acid sites and evaluate their relative importance. We then define a metric, the functional coverage, to measure the 'effective' matching in the amino acid sequence between the vaccine and the parasite. With the variable selection procedure, development of a vaccine needs only to target the important sites, and the potential effectiveness of a vaccine candidate is reflected by the functional coverage. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

SUBMITTER: Hu Z

PROVIDER: S-EPMC4390489 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Vaccine design via nonnegative lasso-based variable selection.

Hu Zonghui Z Follmann Dean A DA Miura Kazutoyo K

Statistics in medicine 20150202 10

There are many different strains of malaria parasites, each represented by a unique sequence of amino acids. A desirable vaccine would match the amino acid sequence of the parasite antigen. Because of the three-dimensional structure of protein, not all sites in the amino acid sequence participate in the binding between the vaccine-induced antibody and the parasite antigen. Nor do all sites have equal importance. In this work, we apply a nonnegative lasso-based variable selection to identify the ...[more]

PMID: 25643693

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Project description:BackgroundAdverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results.MethodsWe propose a new signal detection methodology based on the adaptive lasso. We derived two new adaptive weights from (i) a lasso regression using the Bayesian Information Criterion (BIC), and (ii) the class-imbalanced subsampling lasso (CISL), an extension of stability selection. The BIC is used in the adaptive lasso stage for variable selection. We performed an extensive simulation study and an application to real data, where we compared our methods to the existing adaptive lasso, and recent detection approaches based on lasso regression or propensity scores in high dimension. For both studies, we evaluate the methods in terms of false discoveries and sensitivity.ResultsIn the simulations and the application, both proposed adaptive weights show equivalent or better performances than the other competitors, with an advantage for the CISL-based adaptive weights. CISL and lasso regression using BIC are solid alternatives.ConclusionOur proposed adaptive lasso is an appealing methodology for signal detection in pharmacovigilance. Although we cannot rely on test theory, our approaches show a low and stable False Discovery Rate in all simulation settings. All methods evaluated in this work are implemented in the adapt4pv R package.

Dataset Information

Vaccine design via nonnegative lasso-based variable selection.

Publications

Vaccine design via nonnegative lasso-based variable selection.

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