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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Crossing Over…Markov Meets Mendel
|
|---|---|
| Published in |
PLoS Computational Biology, May 2012
|
| DOI | 10.1371/journal.pcbi.1002462 |
| Pubmed ID | |
| Authors |
Saad Mneimneh |
| Abstract |
Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage-one of the first efforts towards a computational approach to biology-relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendel's laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics). However, other students may easily follow by omitting the mathematically more elaborate parts. I kept those as separate sections in the exposition. |
X Demographics
The data shown below were collected from the profiles of 13 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 | 3 | 23% |
| Greece | 1 | 8% |
| United Kingdom | 1 | 8% |
| Singapore | 1 | 8% |
| Unknown | 7 | 54% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 62% |
| Scientists | 4 | 31% |
| Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 155 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 10 | 6% |
| Germany | 3 | 2% |
| Portugal | 2 | 1% |
| Netherlands | 2 | 1% |
| Brazil | 2 | 1% |
| Uruguay | 1 | <1% |
| France | 1 | <1% |
| Colombia | 1 | <1% |
| Peru | 1 | <1% |
| Other | 3 | 2% |
| Unknown | 129 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 45 | 29% |
| Student > Bachelor | 22 | 14% |
| Student > Ph. D. Student | 21 | 14% |
| Professor > Associate Professor | 15 | 10% |
| Student > Master | 7 | 5% |
| Other | 22 | 14% |
| Unknown | 23 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 59 | 38% |
| Biochemistry, Genetics and Molecular Biology | 34 | 22% |
| Medicine and Dentistry | 7 | 5% |
| Computer Science | 7 | 5% |
| Neuroscience | 4 | 3% |
| Other | 17 | 11% |
| Unknown | 27 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 19 April 2013.
All research outputs
#4,715,107
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#3,724
of 9,043 outputs
Outputs of similar age
#30,420
of 177,542 outputs
Outputs of similar age from PLoS Computational Biology
#31
of 108 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,043 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 58% 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 177,542 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 108 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 71% of its contemporaries.