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Editorial
. 2023 Aug 17;19(8):e1011390.
doi: 10.1371/journal.pcbi.1011390. eCollection 2023 Aug.

The blossoming of methods and software in computational biology

Feilim Mac Gabhann  1 , Virginia E Pitzer  2 , Jason A Papin  3
Affiliations
Editorial

The blossoming of methods and software in computational biology

Feilim Mac Gabhann et al. PLoS Comput Biol. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Number of Methods and Software papers published each year in PLOS Computational Biology, 2013–2022.
The acceptance rates for submitted Methods and Software papers have been consistent at approximately 35% and 45%, respectively, for several years.
See this image and copyright information in PMC

References

    1. Cadwallader L, Papin JA, Mac Gabhann F, Kirk R. Collaborating with our community to increase code sharing. PLoS Comput Biol. 2021;17(3):e1008867. doi: 10.1371/journal.pcbi.1008867 - DOI - PMC - PubMed
    1. McMurdie PJ, Holmes S. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS Comput Biol. 2014;10(4):e1003531. doi: 10.1371/journal.pcbi.1003531 - DOI - PMC - PubMed
    1. Wang S, Sun S, Li Z, Zhang R, Xu J. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model. PLoS Comput Biol. 2017;13(1):e1005324. doi: 10.1371/journal.pcbi.1005324 - DOI - PMC - PubMed
    1. Van Valen DA, Kudo T, Lane KM, Macklin DN, Quach NT, DeFelice MM, et al.. Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments. PLoS Comput Biol. 2016;12(11):e1005177. doi: 10.1371/journal.pcbi.1005177 - DOI - PMC - PubMed
    1. Bouckaert R, Vaughan TG, Barido-Sottani J, Duchêne S, Fourment M, Gavryushkina A, et al.. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol. 2019;15(4):e1006650. doi: 10.1371/journal.pcbi.1006650 - DOI - PMC - PubMed

Publication types