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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Agent-Based Modeling of Cancer Stem Cell Driven Solid Tumor Growth.
|
|---|---|
| Chapter number | 346 |
| Book title |
Stem Cell Heterogeneity
|
| Published in |
Methods in molecular biology, April 2016
|
| DOI | 10.1007/7651_2016_346 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6549-6, 978-1-4939-6550-2
|
| Authors |
Jan Poleszczuk, Paul Macklin, Heiko Enderling, Poleszczuk, Jan, Macklin, Paul, Enderling, Heiko |
| Abstract |
Computational modeling of tumor growth has become an invaluable tool to simulate complex cell-cell interactions and emerging population-level dynamics. Agent-based models are commonly used to describe the behavior and interaction of individual cells in different environments. Behavioral rules can be informed and calibrated by in vitro assays, and emerging population-level dynamics may be validated with both in vitro and in vivo experiments. Here, we describe the design and implementation of a lattice-based agent-based model of cancer stem cell driven tumor growth. |
X Demographics
The data shown below were collected from the profiles of 8 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 | 5 | 63% |
| Poland | 1 | 13% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 63% |
| Members of the public | 3 | 38% |
Mendeley readers
The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 1% |
| France | 1 | 1% |
| Unknown | 69 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 21% |
| Student > Ph. D. Student | 14 | 20% |
| Student > Bachelor | 7 | 10% |
| Student > Master | 7 | 10% |
| Student > Doctoral Student | 4 | 6% |
| Other | 6 | 8% |
| Unknown | 18 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 10% |
| Mathematics | 7 | 10% |
| Engineering | 7 | 10% |
| Agricultural and Biological Sciences | 7 | 10% |
| Computer Science | 7 | 10% |
| Other | 18 | 25% |
| Unknown | 18 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 09 April 2016.
All research outputs
#5,605,767
of 22,860,626 outputs
Outputs from Methods in molecular biology
#1,544
of 13,127 outputs
Outputs of similar age
#79,640
of 300,875 outputs
Outputs of similar age from Methods in molecular biology
#4
of 24 outputs
Altmetric has tracked 22,860,626 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,127 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 88% 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 300,875 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 73% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.