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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A novel hierarchical clustering algorithm for gene sequences
|
|---|---|
| Published in |
BMC Bioinformatics, July 2012
|
| DOI | 10.1186/1471-2105-13-174 |
| Pubmed ID | |
| Authors |
Dan Wei, Qingshan Jiang, Yanjie Wei, Shengrui Wang |
| Abstract |
Clustering DNA sequences into functional groups is an important problem in bioinformatics. We propose a new alignment-free algorithm, mBKM, based on a new distance measure, DMk, for clustering gene sequences. This method transforms DNA sequences into the feature vectors which contain the occurrence, location and order relation of k-tuples in DNA sequence. Afterwards, a hierarchical procedure is applied to clustering DNA sequences based on the feature vectors. |
X Demographics
The data shown below were collected from the profiles of 6 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 | 2 | 33% |
| Sweden | 1 | 17% |
| United Kingdom | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 67% |
| Members of the public | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 135 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| Sweden | 2 | 1% |
| France | 1 | <1% |
| Estonia | 1 | <1% |
| Sri Lanka | 1 | <1% |
| Japan | 1 | <1% |
| Spain | 1 | <1% |
| Unknown | 125 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 43 | 32% |
| Researcher | 23 | 17% |
| Student > Master | 15 | 11% |
| Student > Bachelor | 9 | 7% |
| Professor | 7 | 5% |
| Other | 21 | 16% |
| Unknown | 17 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 46 | 34% |
| Computer Science | 36 | 27% |
| Biochemistry, Genetics and Molecular Biology | 13 | 10% |
| Engineering | 7 | 5% |
| Mathematics | 3 | 2% |
| Other | 13 | 10% |
| Unknown | 17 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 03 August 2017.
All research outputs
#14,253,126
of 25,161,628 outputs
Outputs from BMC Bioinformatics
#3,834
of 7,656 outputs
Outputs of similar age
#93,239
of 170,080 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 89 outputs
Altmetric has tracked 25,161,628 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,656 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 48th percentile – i.e., 48% 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 170,080 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 89 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 61% of its contemporaries.