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. 2020 Nov 10;13(1):521.
doi: 10.1186/s13104-020-05371-0.

FINET: Fast Inferring NETwork

Anyou Wang  1 , Rong Hai  2   3
Affiliations

FINET: Fast Inferring NETwork

Anyou Wang et al. BMC Res Notes. .

Abstract

Objectives: Numerous software has been developed to infer the gene regulatory network, a long-standing key topic in biology and computational biology. Yet the slowness and inaccuracy inherited in current software hamper their applications to the increasing massive data. Here, we develop a software, FINET (Fast Inferring NETwork), to infer a network with high accuracy and rapidity from big data.

Results: The high accuracy results from integrating algorithms with stability-selection, elastic-net, and parameter optimization. Tested by a known biological network, FINET infers interactions with over 94% precision. The high speed comes from partnering parallel computations implemented with Julia, a new compiled language that runs much faster than existing languages used in the current software, such as R, Python, and MATLAB. Regardless of FINET's implementations with Julia, users with no background in the language or computer science can easily operate it, with only a user-friendly single command line. In addition, FINET can infer other networks such as chemical networks and social networks. Overall, FINET provides a confident way to efficiently and accurately infer any type of network for any scale of data.

Keywords: Accuracy; Elastic-net; FINET; Inference; Julia; LASSO; Network; Stability selection.

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Conflict of interest statement

No competing interests.

Figures

Fig. 1
Fig. 1
FINET parameter optimization and performance. a Frequency cutoff optimization. Frequency cutoff from 0.1 to 1.0 vs AUC, precision and normalized true positive calling (true positive callings at each cutoff/max(true positive callings at each cutoff)). This data resulted from FINET running on network1 at dream5 with following settings, m = 4, n = 500, alpha = 0.5 (see github software website for details). b Comparisons of precision of resampling m subgroups (frequency cutoff > 0.95). c, d The overall performance of FINET when m = 8 (c) and 12 (d). e Performance comparison between FINET, ARACNe-AP and C3NET. X-axis lab fo ARACNe-AP and C3NET represent p-value and alpha value, respectively, designed for significant threshold in ARACNe-AP and C3NET, while m in FINET as the number of sub-groups as shown above
See this image and copyright information in PMC

References

    1. El-Brolosy MA, Kontarakis Z, Rossi A, Kuenne C, Günther S, Fukuda N, et al. Genetic compensation triggered by mutant mRNA degradation. Nature. 2019;568:193. doi: 10.1038/s41586-019-1064-z. - DOI - PMC - PubMed
    1. Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, et al. Wisdom of crowds for robust gene network inference. Nat Methods. 2012;9:796–804. doi: 10.1038/nmeth.2016. - DOI - PMC - PubMed
    1. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE. 2010;5:90. doi: 10.1371/journal.pone.0012776. - DOI - PMC - PubMed
    1. Mordelet F, Vert J-P. SIRENE: supervised inference of regulatory networks. Bioinformatics. 2008;24:i76–i82. doi: 10.1093/bioinformatics/btn273. - DOI - PubMed
    1. Haury A-C, Mordelet F, Vera-Licona P, Vert J-P. TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Syst Biol. 2012;6:145. doi: 10.1186/1752-0509-6-145. - DOI - PMC - PubMed