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Mendeley readers
Attention Score in Context
| Title |
Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features
|
|---|---|
| Published in |
BMC Bioinformatics, October 2011
|
| DOI | 10.1186/1471-2105-12-412 |
| Pubmed ID | |
| Authors |
Ozgur Demir-Kavuk, Mayumi Kamada, Tatsuya Akutsu, Ernst-Walter Knapp |
| Abstract |
Machine learning methods are nowadays used for many biological prediction problems involving drugs, ligands or polypeptide segments of a protein. In order to build a prediction model a so called training data set of molecules with measured target properties is needed. For many such problems the size of the training data set is limited as measurements have to be performed in a wet lab. Furthermore, the considered problems are often complex, such that it is not clear which molecular descriptors (features) may be suitable to establish a strong correlation with the target property. In many applications all available descriptors are used. This can lead to difficult machine learning problems, when thousands of descriptors are considered and only few (e.g. below hundred) molecules are available for training. |
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 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Malaysia | 1 | <1% |
| Colombia | 1 | <1% |
| Spain | 1 | <1% |
| Sweden | 1 | <1% |
| Unknown | 99 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 16% |
| Researcher | 15 | 14% |
| Student > Master | 13 | 12% |
| Student > Bachelor | 12 | 11% |
| Professor | 4 | 4% |
| Other | 16 | 15% |
| Unknown | 28 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 20 | 19% |
| Engineering | 12 | 11% |
| Agricultural and Biological Sciences | 10 | 10% |
| Biochemistry, Genetics and Molecular Biology | 5 | 5% |
| Mathematics | 5 | 5% |
| Other | 16 | 15% |
| Unknown | 37 | 35% |
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 26 October 2011.
All research outputs
#15,314,171
of 22,776,824 outputs
Outputs from BMC Bioinformatics
#5,373
of 7,276 outputs
Outputs of similar age
#95,943
of 140,540 outputs
Outputs of similar age from BMC Bioinformatics
#73
of 102 outputs
Altmetric has tracked 22,776,824 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 7,276 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 18th percentile – i.e., 18% 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 140,540 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 102 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.