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Attention Score in Context
| Title |
Isomorphism and similarity for 2-generation pedigrees
|
|---|---|
| Published in |
BMC Bioinformatics, March 2015
|
| DOI | 10.1186/1471-2105-16-s5-s7 |
| Pubmed ID | |
| Authors |
Haitao Jiang, Guohui Lin, Weitian Tong, Daming Zhu, Binhai Zhu |
| Abstract |
We consider the emerging problem of comparing the similarity between (unlabeled) pedigrees. More specifically, we focus on the simplest pedigrees, namely, the 2-generation pedigrees. We show that the isomorphism testing for two 2-generation pedigrees is GI-hard. If the 2-generation pedigrees are monogamous (i.e., each individual at level-1 can mate with exactly one partner) then the isomorphism testing problem can be solved in polynomial time. We then consider the problem by relaxing it into an NP-complete decomposition problem which can be formulated as the Minimum Common Integer Pair Partition (MCIPP) problem, which we show to be FPT by exploiting a property of the optimal solution. While there is still some difficulty to overcome, this lays down a solid foundation for this research. |
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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 2 | 29% |
| Researcher | 2 | 29% |
| Student > Ph. D. Student | 1 | 14% |
| Lecturer | 1 | 14% |
| Unknown | 1 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 57% |
| Biochemistry, Genetics and Molecular Biology | 2 | 29% |
| Agricultural and Biological Sciences | 1 | 14% |
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 29 January 2017.
All research outputs
#15,437,553
of 22,947,506 outputs
Outputs from BMC Bioinformatics
#5,391
of 7,308 outputs
Outputs of similar age
#171,146
of 286,531 outputs
Outputs of similar age from BMC Bioinformatics
#108
of 141 outputs
Altmetric has tracked 22,947,506 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 7,308 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.
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We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.