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INBIA: a boosting methodology for proteomic network inference

Overview of attention for article published in BMC Bioinformatics, July 2018
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Title
INBIA: a boosting methodology for proteomic network inference
Published in
BMC Bioinformatics, July 2018
DOI 10.1186/s12859-018-2183-5
Pubmed ID
Authors

Davide S. Sardina, Giovanni Micale, Alfredo Ferro, Alfredo Pulvirenti, Rosalba Giugno

Abstract

The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing. We have developed a methodology called Inference Network Based on iRefIndex Analysis (INBIA) to accurately correlate proteomic inferred relations to protein-protein interaction (PPI) networks. INBIA makes use of 14 network inference methods on protein expressions related to 16 cancer types. It uses as reference model the iRefIndex human PPI network. Predictions are validated through non-interacting and tissue specific PPI networks resources. The first, Negatome, takes into account likely non-interacting proteins by combining both structure properties and literature mining. The latter, TissueNet and GIANT, report experimentally verified PPIs in more than 50 human tissues. The reliability of the proposed methodology is assessed by comparing INBIA with PERA, a tool which infers protein interaction networks from Pathway Commons, by both functional and topological analysis. Results show that INBIA is a valuable approach to predict proteomic interactions in pathological conditions starting from the current knowledge of human protein interactions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Student > Bachelor 1 20%
Researcher 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 2 40%
Biochemistry, Genetics and Molecular Biology 1 20%
Engineering 1 20%
Unknown 1 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 18 May 2019.
All research outputs
#15,012,809
of 23,094,276 outputs
Outputs from BMC Bioinformatics
#5,081
of 7,328 outputs
Outputs of similar age
#197,397
of 326,642 outputs
Outputs of similar age from BMC Bioinformatics
#65
of 108 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,328 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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 326,642 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.