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      A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression

      research-article
      1 , 2 , , 1 , 2 , 1 , 2 , 1 , 1 , 2
      BMC Bioinformatics
      BioMed Central
      11th International Multiconference “Bioinformatics of Genome Regulation and Structure\Systems Biology” - BGRS\SB-2018
      20-25 August 2018
      ANDSystem, Associative gene networks, Tissue-specific gene expression, Extrinsic apoptotic signaling pathway, Automated extraction of knowledge, Text-mining

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          Abstract

          Background

          Consideration of tissue-specific gene expression in reconstruction and analysis of molecular genetic networks is necessary for a proper description of the processes occurring in a specified tissue. Currently, there are a number of computer systems that allow the user to reconstruct molecular-genetic networks using the data automatically extracted from the texts of scientific publications. Examples of such systems are STRING, Pathway Commons, MetaCore and Ingenuity. The MetaCore and Ingenuity systems permit taking into account tissue-specific gene expression during the reconstruction of gene networks. Previously, we developed the ANDSystem tool, which also provides an automated extraction of knowledge from scientific texts and allows the reconstruction of gene networks. The main difference between our system and other tools is in the different types of interactions between objects, which makes the ANDSystem complementary to existing well-known systems. However, previous versions of the ANDSystem did not contain any information on tissue-specific expression.

          Results

          A new version of the ANDSystem has been developed. It offers the reconstruction of associative gene networks while taking into account the tissue-specific gene expression. The ANDSystem knowledge base features information on tissue-specific expression for 272 tissues. The system allows the reconstruction of combined gene networks, as well as performing the filtering of genes from such networks using the information on their tissue-specific expression. As an example of the application of such filtering, the gene network of the extrinsic apoptotic signaling pathway was analyzed. It was shown that considering different tissues can lead to changes in gene network structure, including changes in such indicators as betweenness centrality of vertices, clustering coefficient, network centralization, network density, etc.

          Conclusions

          The consideration of tissue specificity can play an important role in the analysis of gene networks, in particular solving the problem of finding the most significant central genes. Thus, the new version of ANDSystem can be employed for a wide range of tasks related to biomedical studies of individual tissues. It is available at http://www-bionet.sscc.ru/and/cell/.

          Electronic supplementary material

          The online version of this article (10.1186/s12859-018-2567-6) contains supplementary material, which is available to authorized users.

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          Most cited references44

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          Solving the multiple instance problem with axis-parallel rectangles

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            PubTator: a web-based text mining tool for assisting biocuration

            Manually curating knowledge from biomedical literature into structured databases is highly expensive and time-consuming, making it difficult to keep pace with the rapid growth of the literature. There is therefore a pressing need to assist biocuration with automated text mining tools. Here, we describe PubTator, a web-based system for assisting biocuration. PubTator is different from the few existing tools by featuring a PubMed-like interface, which many biocurators find familiar, and being equipped with multiple challenge-winning text mining algorithms to ensure the quality of its automatic results. Through a formal evaluation with two external user groups, PubTator was shown to be capable of improving both the efficiency and accuracy of manual curation. PubTator is publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/.
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              Understanding network concepts in modules

              Background Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory. Results Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks. Conclusion Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks
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                Author and article information

                Contributors
                salix@bionet.nsc.ru
                demps@bionet.nsc.ru
                itv@bionet.nsc.ru
                elmish@bionet.nsc.ru
                saik@bionet.nsc.ru
                Conference
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                5 February 2019
                5 February 2019
                2019
                : 20
                Issue : Suppl 1 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
                : 34
                Affiliations
                [1 ]ISNI 0000 0001 2192 9124, GRID grid.4886.2, Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, , Russian Academy of Sciences, ; Prospekt Lavrentyeva 10, Novosibirsk, 630090 Russia
                [2 ]ISNI 0000000121896553, GRID grid.4605.7, Novosibirsk State University, ; st. Pirogova 1, Novosibirsk, 630090 Russia
                Article
                2567
                10.1186/s12859-018-2567-6
                6362586
                30717676
                f75176f2-4428-4726-b8f4-eec6ad29fb96
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                11th International Multiconference “Bioinformatics of Genome Regulation and Structure\Systems Biology” - BGRS\SB-2018
                Novosibirsk, Russia
                20-25 August 2018
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                © The Author(s) 2019

                Bioinformatics & Computational biology
                andsystem,associative gene networks,tissue-specific gene expression,extrinsic apoptotic signaling pathway,automated extraction of knowledge,text-mining

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