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Mendeley readers
Attention Score in Context
| Title |
Interpreting linear support vector machine models with heat map molecule coloring
|
|---|---|
| Published in |
Journal of Cheminformatics, March 2011
|
| DOI | 10.1186/1758-2946-3-11 |
| Pubmed ID | |
| Authors |
Lars Rosenbaum, Georg Hinselmann, Andreas Jahn, Andreas Zell |
| Abstract |
Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. |
Mendeley readers
The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 2% |
| Germany | 2 | 2% |
| India | 1 | 1% |
| Unknown | 76 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 19% |
| Student > Ph. D. Student | 13 | 16% |
| Student > Master | 8 | 10% |
| Student > Bachelor | 8 | 10% |
| Other | 7 | 9% |
| Other | 13 | 16% |
| Unknown | 17 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 17 | 21% |
| Computer Science | 14 | 17% |
| Agricultural and Biological Sciences | 10 | 12% |
| Engineering | 7 | 9% |
| Medicine and Dentistry | 2 | 2% |
| Other | 11 | 14% |
| Unknown | 20 | 25% |
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 01 March 2012.
All research outputs
#15,242,272
of 22,663,150 outputs
Outputs from Journal of Cheminformatics
#743
of 825 outputs
Outputs of similar age
#84,556
of 108,423 outputs
Outputs of similar age from Journal of Cheminformatics
#8
of 13 outputs
Altmetric has tracked 22,663,150 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 825 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 5th percentile – i.e., 5% 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 108,423 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.