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Bayesian inference for biomarker discovery in proteomics: an analytic solution

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, July 2017
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Title
Bayesian inference for biomarker discovery in proteomics: an analytic solution
Published in
EURASIP Journal on Bioinformatics & Systems Biology, July 2017
DOI 10.1186/s13637-017-0062-4
Pubmed ID
Authors

Noura Dridi, Audrey Giremus, Jean-Francois Giovannelli, Caroline Truntzer, Melita Hadzagic, Jean-Philippe Charrier, Laurent Gerfault, Patrick Ducoroy, Bruno Lacroix, Pierre Grangeat, Pascal Roy

Abstract

This paper addresses the question of biomarker discovery in proteomics. Given clinical data regarding a list of proteins for a set of individuals, the tackled problem is to extract a short subset of proteins the concentrations of which are an indicator of the biological status (healthy or pathological). In this paper, it is formulated as a specific instance of variable selection. The originality is that the proteins are not investigated one after the other but the best partition between discriminant and non-discriminant proteins is directly sought. In this way, correlations between the proteins are intrinsically taken into account in the decision. The developed strategy is derived in a Bayesian setting, and the decision is optimal in the sense that it minimizes a global mean error. It is finally based on the posterior probabilities of the partitions. The main difficulty is to calculate these probabilities since they are based on the so-called evidence that require marginalization of all the unknown model parameters. Two models are presented that relate the status to the protein concentrations, depending whether the latter are biomarkers or not. The first model accounts for biological variabilities by assuming that the concentrations are Gaussian distributed with a mean and a covariance matrix that depend on the status only for the biomarkers. The second one is an extension that also takes into account the technical variabilities that may significantly impact the observed concentrations. The main contributions of the paper are: (1) a new Bayesian formulation of the biomarker selection problem, (2) the closed-form expression of the posterior probabilities in the noiseless case, and (3) a suitable approximated solution in the noisy case. The methods are numerically assessed and compared to the state-of-the-art methods (t test, LASSO, Battacharyya distance, FOHSIC) on synthetic and real data from proteins quantified in human serum by mass spectrometry in selected reaction monitoring mode.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 27%
Student > Bachelor 3 20%
Other 2 13%
Student > Master 2 13%
Student > Ph. D. Student 1 7%
Other 2 13%
Unknown 1 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 53%
Medicine and Dentistry 2 13%
Nursing and Health Professions 1 7%
Psychology 1 7%
Agricultural and Biological Sciences 1 7%
Other 0 0%
Unknown 2 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 July 2017.
All research outputs
#11,076,455
of 12,457,990 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#39
of 51 outputs
Outputs of similar age
#219,938
of 259,377 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#1
of 2 outputs
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So far Altmetric has tracked 51 research outputs from this source. They receive a mean Attention Score of 1.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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