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Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods

Overview of attention for article published in BMC Bioinformatics, October 2017
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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3 X users
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2 patents

Citations

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25 Dimensions

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57 Mendeley
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Title
Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods
Published in
BMC Bioinformatics, October 2017
DOI 10.1186/s12859-017-1854-y
Pubmed ID
Authors

Marco Notaro, Max Schubach, Peter N. Robinson, Giorgio Valentini

Abstract

The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 21%
Researcher 11 19%
Student > Ph. D. Student 10 18%
Student > Bachelor 3 5%
Professor 3 5%
Other 9 16%
Unknown 9 16%
Readers by discipline Count As %
Computer Science 18 32%
Biochemistry, Genetics and Molecular Biology 7 12%
Medicine and Dentistry 5 9%
Agricultural and Biological Sciences 4 7%
Engineering 4 7%
Other 6 11%
Unknown 13 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 23 January 2024.
All research outputs
#6,513,846
of 23,509,253 outputs
Outputs from BMC Bioinformatics
#2,456
of 7,404 outputs
Outputs of similar age
#104,445
of 325,886 outputs
Outputs of similar age from BMC Bioinformatics
#39
of 117 outputs
Altmetric has tracked 23,509,253 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,404 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 66% 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 325,886 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 67% of its contemporaries.
We're also able to compare this research output to 117 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.