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PhysiCell training apps: Cloud hosted open-source apps to learn cell-based simulation software

Sundus, A.; Kurtoglu, F.; Konstantinopoulos, K.; Chen, M.; Willis, D.; Heiland, R.; Macklin, P.

2022-06-28 systems biology
10.1101/2022.06.24.497566 bioRxiv
Show abstract

Cell-based tissue simulations require not only the ability to write new code in a simulation framework, but also an understanding of underlying mathematical models, background biology, and parameters for each behavior of an agent. This can entail a steep learning curve for interdisciplinary researchers joining computational biology research. We have created a suite of cloud-hosted open-source tools to separately explore and learn key components of an agent-based cellular simulation framework. This creates an self-contained environment to learn and test functions of cells and the micro-environment in a modular fashion before creating more detailed, research-focused simulation models.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
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Top 2%
17.2%
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2
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3
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50% of probability mass above
5
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4.1%
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6
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Top 0.5%
3.3%
7
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Top 0.7%
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Top 40%
2.5%
9
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Top 2%
2.5%
10
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Top 3%
2.1%
11
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Top 0.1%
1.7%
12
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1.7%
13
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Top 50%
1.7%
14
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○ 33 papers in training set
Top 0.4%
1.1%
15
Scientific Reports
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Top 62%
1.1%
16
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○ 10 papers in training set
Top 0.1%
1.0%
17
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Top 0.6%
0.9%
18
Nature Communications
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Top 55%
0.9%
19
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Top 9%
0.9%
20
Biophysical Journal
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Top 4%
0.9%
21
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Top 4%
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22
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Top 5%
0.6%
23
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24
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○ 165 papers in training set
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25
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26
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○ 31 papers in training set
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0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.