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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improved Chou-Fasman method for protein secondary structure prediction
|
|---|---|
| Published in |
BMC Bioinformatics, December 2006
|
| DOI | 10.1186/1471-2105-7-s4-s14 |
| Pubmed ID | |
| Authors |
Hang Chen, Fei Gu, Zhengge Huang |
| Abstract |
Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. The prediction technique has been developed for several decades. The Chou-Fasman algorithm, one of the earliest methods, has been successfully applied to the prediction. However, this method has its limitations due to low accuracy, unreliable parameters, and over prediction. Thanks to the recent development in protein folding type-specific structure propensities and wavelet transformation, the shortcomings in Chou-Fasman method are able to be overcome. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| India | 2 | 2% |
| Colombia | 1 | 1% |
| Taiwan | 1 | 1% |
| Brazil | 1 | 1% |
| Greece | 1 | 1% |
| Mexico | 1 | 1% |
| Unknown | 90 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 23 | 23% |
| Student > Ph. D. Student | 17 | 17% |
| Student > Master | 16 | 16% |
| Researcher | 13 | 13% |
| Professor > Associate Professor | 6 | 6% |
| Other | 11 | 11% |
| Unknown | 14 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 25 | 25% |
| Agricultural and Biological Sciences | 25 | 25% |
| Chemistry | 10 | 10% |
| Medicine and Dentistry | 6 | 6% |
| Computer Science | 5 | 5% |
| Other | 11 | 11% |
| Unknown | 18 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 29 July 2022.
All research outputs
#6,482,068
of 22,986,950 outputs
Outputs from BMC Bioinformatics
#2,490
of 7,309 outputs
Outputs of similar age
#34,866
of 157,084 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 55 outputs
Altmetric has tracked 22,986,950 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 7,309 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 64% of its peers.
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 157,084 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 74% of its contemporaries.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.