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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Data Mining Techniques for the Life Sciences
|
|---|---|
| Chapter number | 9 |
| Book title |
Data Mining Techniques for the Life Sciences
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3572-7_9 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3570-3, 978-1-4939-3572-7
|
| Authors |
Patthy, László, László Patthy |
| Editors |
Oliviero Carugo, Frank Eisenhaber |
| Abstract |
Correct prediction of the structure of protein-coding genes of higher eukaryotes is a difficult task therefore public sequence databases incorporating predicted sequences are increasingly contaminated with erroneous sequences. The high rate of misprediction has serious consequences since it significantly affects the conclusions that may be drawn from genome-scale sequence analyses.Here we describe the MisPred and FixPred approaches that may help the identification and correction of erroneous sequences. The rationale of these approaches is that a protein sequence is likely to be erroneous if some of its features conflict with our current knowledge about proteins. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 44% |
| Other | 1 | 11% |
| Student > Bachelor | 1 | 11% |
| Student > Doctoral Student | 1 | 11% |
| Student > Master | 1 | 11% |
| Other | 1 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 56% |
| Medicine and Dentistry | 2 | 22% |
| Biochemistry, Genetics and Molecular Biology | 1 | 11% |
| Engineering | 1 | 11% |
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 27 April 2016.
All research outputs
#15,369,653
of 22,865,319 outputs
Outputs from Methods in molecular biology
#5,350
of 13,127 outputs
Outputs of similar age
#230,936
of 393,648 outputs
Outputs of similar age from Methods in molecular biology
#545
of 1,470 outputs
Altmetric has tracked 22,865,319 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 13,127 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% 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 393,648 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,470 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.