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A strategy for multimodal data integration: application to biomarkers identification in spinocerebellar ataxia

Overview of attention for article published in Briefings in Bioinformatics, July 2017
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Citations

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47 Dimensions

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64 Mendeley
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Title
A strategy for multimodal data integration: application to biomarkers identification in spinocerebellar ataxia
Published in
Briefings in Bioinformatics, July 2017
DOI 10.1093/bib/bbx060
Pubmed ID
Authors

Imene Garali, Isaac M Adanyeguh, Farid Ichou, Vincent Perlbarg, Alexandre Seyer, Benoit Colsch, Ivan Moszer, Vincent Guillemot, Alexandra Durr, Fanny Mochel, Arthur Tenenhaus

Abstract

The growing number of modalities (e.g. multi-omics, imaging and clinical data) characterizing a given disease provides physicians and statisticians with complementary facets reflecting the disease process but emphasizes the need for novel statistical methods of data analysis able to unify these views. Such data sets are indeed intrinsically structured in blocks, where each block represents a set of variables observed on a group of individuals. Therefore, classical statistical tools cannot be applied without altering their organization, with the risk of information loss. Regularized generalized canonical correlation analysis (RGCCA) and its sparse generalized canonical correlation analysis (SGCCA) counterpart are component-based methods for exploratory analyses of data sets structured in blocks of variables. Rather than operating sequentially on parts of the measurements, the RGCCA/SGCCA-based integrative analysis method aims at summarizing the relevant information between and within the blocks. It processes a priori information defining which blocks are supposed to be linked to one another, thus reflecting hypotheses about the biology underlying the data blocks. It also requires the setting of extra parameters that need to be carefully adjusted.Here, we provide practical guidelines for the use of RGCCA/SGCCA. We also illustrate the flexibility and usefulness of RGCCA/SGCCA on a unique cohort of patients with four genetic subtypes of spinocerebellar ataxia, in which we obtained multiple data sets from brain volumetry and magnetic resonance spectroscopy, and metabolomic and lipidomic analyses. As a first step toward the extraction of multimodal biomarkers, and through the reduction to a few meaningful components and the visualization of relevant variables, we identified possible markers of disease progression.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 14%
Researcher 8 13%
Student > Master 8 13%
Student > Doctoral Student 6 9%
Student > Bachelor 4 6%
Other 17 27%
Unknown 12 19%
Readers by discipline Count As %
Computer Science 8 13%
Biochemistry, Genetics and Molecular Biology 7 11%
Agricultural and Biological Sciences 7 11%
Neuroscience 4 6%
Immunology and Microbiology 3 5%
Other 18 28%
Unknown 17 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 05 July 2018.
All research outputs
#12,752,767
of 22,986,950 outputs
Outputs from Briefings in Bioinformatics
#1,195
of 2,673 outputs
Outputs of similar age
#143,821
of 313,819 outputs
Outputs of similar age from Briefings in Bioinformatics
#22
of 41 outputs
Altmetric has tracked 22,986,950 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,673 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has gotten more attention than average, scoring higher than 54% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 313,819 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.