↓ Skip to main content

Review of stochastic hybrid systems with applications in biological systems modeling and analysis

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, June 2017
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
29 Mendeley
Title
Review of stochastic hybrid systems with applications in biological systems modeling and analysis
Published in
EURASIP Journal on Bioinformatics & Systems Biology, June 2017
DOI 10.1186/s13637-017-0061-5
Pubmed ID
Authors

Xiangfang Li, Oluwaseyi Omotere, Lijun Qian, Edward R. Dougherty

Abstract

Stochastic hybrid systems (SHS) have attracted a lot of research interests in recent years. In this paper, we review some of the recent applications of SHS to biological systems modeling and analysis. Due to the nature of molecular interactions, many biological processes can be conveniently described as a mixture of continuous and discrete phenomena employing SHS models. With the advancement of SHS theory, it is expected that insights can be obtained about biological processes such as drug effects on gene regulation. Furthermore, combining with advanced experimental methods, in silico simulations using SHS modeling techniques can be carried out for massive and rapid verification or falsification of biological hypotheses. The hope is to substitute costly and time-consuming in vitro or in vivo experiments or provide guidance for those experiments and generate better hypotheses.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 24%
Researcher 5 17%
Professor > Associate Professor 3 10%
Student > Master 3 10%
Student > Bachelor 1 3%
Other 2 7%
Unknown 8 28%
Readers by discipline Count As %
Engineering 8 28%
Biochemistry, Genetics and Molecular Biology 4 14%
Mathematics 3 10%
Medicine and Dentistry 2 7%
Energy 1 3%
Other 3 10%
Unknown 8 28%
Attention Score in Context

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 01 July 2017.
All research outputs
#22,764,772
of 25,382,440 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#42
of 53 outputs
Outputs of similar age
#286,713
of 327,487 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#1
of 2 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 53 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 1st percentile – i.e., 1% 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 327,487 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them