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Impact of similarity threshold on the topology of molecular similarity networks and clustering outcomes

Overview of attention for article published in Journal of Cheminformatics, March 2016
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  • Above-average Attention Score compared to outputs of the same age (62nd percentile)

Mentioned by

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3 tweeters

Citations

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9 Dimensions

Readers on

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41 Mendeley
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Title
Impact of similarity threshold on the topology of molecular similarity networks and clustering outcomes
Published in
Journal of Cheminformatics, March 2016
DOI 10.1186/s13321-016-0127-5
Pubmed ID
Authors

Gergely Zahoránszky-Kőhalmi, Cristian G. Bologa, Tudor I. Oprea

Abstract

Complex network theory based methods and the emergence of "Big Data" have reshaped the terrain of investigating structure-activity relationships of molecules. This change gave rise to new methods which need to face an important challenge, namely: how to restructure a large molecular dataset into a network that best serves the purpose of the subsequent analyses. With special focus on network clustering, our study addresses this open question by proposing a data transformation method and a clustering framework. Using the WOMBAT and PubChem MLSMR datasets we investigated the relation between varying the similarity threshold applied on the similarity matrix and the average clustering coefficient of the emerging similarity-based networks. These similarity networks were then clustered with the InfoMap algorithm. We devised a systematic method to generate so-called "pseudo-reference" clustering datasets which compensate for the lack of large-scale reference datasets. With help from the clustering framework we were able to observe the effects of varying the similarity threshold and its consequence on the average clustering coefficient and the clustering performance. We observed that the average clustering coefficient versus similarity threshold function can be characterized by the presence of a peak that covers a range of similarity threshold values. This peak is preceded by a steep decline in the number of edges of the similarity network. The maximum of this peak is well aligned with the best clustering outcome. Thus, if no reference set is available, choosing the similarity threshold associated with this peak would be a near-ideal setting for the subsequent network cluster analysis. The proposed method can be used as a general approach to determine the appropriate similarity threshold to generate the similarity network of large-scale molecular datasets.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 2%
Bulgaria 1 2%
Brazil 1 2%
Unknown 38 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 24%
Student > Ph. D. Student 9 22%
Researcher 7 17%
Student > Doctoral Student 4 10%
Lecturer 3 7%
Other 6 15%
Unknown 2 5%
Readers by discipline Count As %
Chemistry 10 24%
Computer Science 9 22%
Agricultural and Biological Sciences 5 12%
Engineering 2 5%
Biochemistry, Genetics and Molecular Biology 2 5%
Other 8 20%
Unknown 5 12%

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 13 June 2016.
All research outputs
#3,149,933
of 7,890,722 outputs
Outputs from Journal of Cheminformatics
#231
of 377 outputs
Outputs of similar age
#100,393
of 276,606 outputs
Outputs of similar age from Journal of Cheminformatics
#8
of 11 outputs
Altmetric has tracked 7,890,722 research outputs across all sources so far. This one has received more attention than most of these and is in the 59th percentile.
So far Altmetric has tracked 377 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one is in the 37th percentile – i.e., 37% 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 276,606 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 62% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.