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How to test bioinformatics software?

Overview of attention for article published in Biophysical Reviews, August 2015
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Title
How to test bioinformatics software?
Published in
Biophysical Reviews, August 2015
DOI 10.1007/s12551-015-0177-3
Pubmed ID
Authors

Amir Hossein Kamali, Eleni Giannoulatou, Tsong Yueh Chen, Michael A. Charleston, Alistair L. McEwan, Joshua W. K. Ho

Abstract

Bioinformatics is the application of computational, mathematical and statistical techniques to solve problems in biology and medicine. Bioinformatics programs developed for computational simulation and large-scale data analysis are widely used in almost all areas of biophysics. The appropriate choice of algorithms and correct implementation of these algorithms are critical for obtaining reliable computational results. Nonetheless, it is often very difficult to systematically test these programs as it is often hard to verify the correctness of the output, and to effectively generate failure-revealing test cases. Software testing is an important process of verification and validation of scientific software, but very few studies have directly dealt with the issues of bioinformatics software testing. In this work, we review important concepts and state-of-the-art methods in the field of software testing. We also discuss recent reports on adapting and implementing software testing methodologies in the bioinformatics field, with specific examples drawn from systems biology and genomic medicine.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users 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 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 55 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 11 20%
Student > Master 8 15%
Researcher 7 13%
Student > Ph. D. Student 7 13%
Student > Postgraduate 2 4%
Other 7 13%
Unknown 13 24%
Readers by discipline Count As %
Computer Science 10 18%
Biochemistry, Genetics and Molecular Biology 10 18%
Agricultural and Biological Sciences 10 18%
Unspecified 1 2%
Business, Management and Accounting 1 2%
Other 6 11%
Unknown 17 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 21 March 2023.
All research outputs
#14,427,926
of 23,567,572 outputs
Outputs from Biophysical Reviews
#248
of 804 outputs
Outputs of similar age
#133,485
of 265,715 outputs
Outputs of similar age from Biophysical Reviews
#1
of 2 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 804 research outputs from this source. They receive a mean Attention Score of 2.8. This one has gotten more attention than average, scoring higher than 65% 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 265,715 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% 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