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X Demographics
Mendeley readers
Attention Score in Context
| Title |
From sequence to enzyme mechanism using multi-label machine learning
|
|---|---|
| Published in |
BMC Bioinformatics, May 2014
|
| DOI | 10.1186/1471-2105-15-150 |
| Pubmed ID | |
| Authors |
Luna De Ferrari, John BO Mitchell |
| Abstract |
In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 25% |
| United Kingdom | 1 | 25% |
| Norway | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| France | 1 | 1% |
| Norway | 1 | 1% |
| Unknown | 76 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 23 | 29% |
| Student > Ph. D. Student | 16 | 20% |
| Student > Master | 9 | 11% |
| Student > Bachelor | 6 | 8% |
| Lecturer | 2 | 3% |
| Other | 7 | 9% |
| Unknown | 16 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 14 | 18% |
| Agricultural and Biological Sciences | 13 | 16% |
| Chemistry | 9 | 11% |
| Computer Science | 8 | 10% |
| Engineering | 2 | 3% |
| Other | 10 | 13% |
| Unknown | 23 | 29% |
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 30 May 2014.
All research outputs
#14,196,440
of 22,756,196 outputs
Outputs from BMC Bioinformatics
#4,721
of 7,271 outputs
Outputs of similar age
#119,609
of 227,120 outputs
Outputs of similar age from BMC Bioinformatics
#83
of 151 outputs
Altmetric has tracked 22,756,196 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,271 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 30th percentile – i.e., 30% 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 227,120 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 151 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.