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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Structure learning for bayesian networks as models of biological networks.
|
|---|---|
| Chapter number | 4 |
| Book title |
Data Mining for Systems Biology
|
| Published in |
Methods in molecular biology, December 2012
|
| DOI | 10.1007/978-1-62703-107-3_4 |
| Pubmed ID | |
| Book ISBNs |
978-1-62703-106-6, 978-1-62703-107-3
|
| Authors |
Larjo A, Shmulevich I, Lähdesmäki H, Antti Larjo, Ilya Shmulevich, Harri Lähdesmäki, Larjo, Antti, Shmulevich, Ilya, Lähdesmäki, Harri |
| Abstract |
Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data. |
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 % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 5% |
| India | 1 | 2% |
| Portugal | 1 | 2% |
| Unknown | 39 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 33% |
| Student > Ph. D. Student | 12 | 28% |
| Student > Master | 4 | 9% |
| Student > Doctoral Student | 3 | 7% |
| Other | 3 | 7% |
| Other | 4 | 9% |
| Unknown | 3 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 33% |
| Biochemistry, Genetics and Molecular Biology | 12 | 28% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 7% |
| Medicine and Dentistry | 3 | 7% |
| Computer Science | 3 | 7% |
| Other | 3 | 7% |
| Unknown | 5 | 12% |
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 16 March 2014.
All research outputs
#18,369,403
of 22,751,628 outputs
Outputs from Methods in molecular biology
#7,861
of 13,089 outputs
Outputs of similar age
#215,348
of 277,400 outputs
Outputs of similar age from Methods in molecular biology
#226
of 352 outputs
Altmetric has tracked 22,751,628 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,089 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 24th percentile – i.e., 24% 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 277,400 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 352 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.