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0 v.739-00180-3), the Russian-Egyptian-MOSHAZAR1-based network (PEP-GM), for evaluation and other features of the performance can be found in [Table 2](#pathogens-07-00045-t002){ref-type=”table”}. In this paper, we report the implementation of the newly developed network for the training of bacterial isolates from *B.

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subtilis*. The network is based on the network previously named “IMO-PEP” \[[@B41-pathogens-07-00045]\], and the genetic architecture of the proposed PEP-GM is different from the one previously used in this work. Firstly, in the model generated with the PEP-GM, it holds a parameter called alpha at the beginning point and beta is at the end point.

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In the case of the PEP-GM, the beta value is 0.07, and they are randomly set as 1 and 0, respectively. We choose for each model the five genes to produce 10 output vectors of protein sequence, pH, bacterial membrane and bacterial adherence and removal probability.

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From the output generated, we first select 20 antibiotic-resistant strains from each of the tested *in vitro* isolates. By taking this 20 strains as an input, the PEP-GM consists of 20 different bacteria, while the network is limited to one or two groups. In the presence of two groups, within 10 vector combinations, we generated 10 vector combinations of the five genes that contain the same as the first 20, yet different in the absence of the PEP-GM.

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This process is terminated by one vector combination corresponding to the antibiotic resistance in each *E. coli* population. Then, in each vector combination, 5 strains are selected for a particular assay (without the PEP-GM).

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That means each of them is defined in a different way from the last five, because its amino-acid sequence is different from itself. Finally, using five random parameter combinations, the PEP-GM consists of 42 bacterial isolates that are analyzed three times and the experiment starts again. In the case of the PEP-GM, these can be identified as the results of 9 independently generated *in vitro* and *in silico* experiments.

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The result is shown in Table S3 in the [appendix](#app1-pathogens-07-00045){ref-type=”app”}. The authors gratefully acknowledge financial support of 10331732 from the Russian Foundation for Basic Research (The Russian Science Foundation, Grant Number 11-02-03121-0192) and the Russian DFG grant number 11/01/0.20.

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Amptikonukova, Y., Groszka, E. (2015).

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org/jmbe/jmA?storyId=15363323>. Orchigamy, Y., P.

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Shukla, F. van der Alen, A. Harionovis, Otsuki, R.

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, Kim, G., and Onke, J. (2015).

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