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      Creation of Individual Scientific Concept-Centered Semantic Maps Based on Automated Text-Mining Analysis of PubMed

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          Abstract

          Concept-centered semantic maps were created based on a text-mining analysis of PubMed using the BiblioEngine_v2018 software. The objects (“concepts”) of a semantic map can be MeSH-terms or other terms (names of proteins, diseases, chemical compounds, etc.) structured in the form of controlled vocabularies. The edges between the two objects were automatically calculated based on the index of semantic similarity, which is proportional to the number of publications related to both objects simultaneously. On the one hand, an individual semantic map created based on the already published papers allows us to trace scientific inquiry. On the other hand, a prospective analysis based on the study of PubMed search history enables us to determine the possible directions for future research.

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          A Computer Program for Classifying Plants.

          D J Rogers, T T Tanimoto (1960)
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            GoPubMed: exploring PubMed with the Gene Ontology

            Andreas Doms, Michael Schroeder (2005)
            The biomedical literature grows at a tremendous rate and PubMed comprises already over 15 000 000 abstracts. Finding relevant literature is an important and difficult problem. We introduce GoPubMed, a web server which allows users to explore PubMed search results with the Gene Ontology (GO), a hierarchically structured vocabulary for molecular biology. GoPubMed provides the following benefits: first, it gives an overview of the literature abstracts by categorizing abstracts according to the GO and thus allowing users to quickly navigate through the abstracts by category. Second, it automatically shows general ontology terms related to the original query, which often do not even appear directly in the abstract. Third, it enables users to verify its classification because GO terms are highlighted in the abstracts and as each term is labelled with an accuracy percentage. Fourth, exploring PubMed abstracts with GoPubMed is useful as it shows definitions of GO terms without the need for further look up. GoPubMed is online at . Querying is currently limited to 100 papers per query.
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              Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database.

              Philip Andrews, Valerie Wasinger, Tom W Blackwell … (2005)
              HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anti-coagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay-based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs. We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches. This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.
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                Author and article information

                Contributors
                Elena Ponomarenko:
                ORCID: http://orcid.org/0000-0001-8527-6003
                Journal
                Journal ID (nlm-ta): Adv Bioinformatics
                Journal ID (iso-abbrev): Adv Bioinformatics
                Journal ID (publisher-id): ABI
                Title: Advances in Bioinformatics
                Publisher: Hindawi
                ISSN (Print): 1687-8027
                ISSN (Electronic): 1687-8035
                Publication date Collection: 2018
                Publication date (Electronic): 26 July 2018
                Volume: 2018
                Electronic Location Identifier: 4625394
                Affiliations
                1Institute of Biomedical Chemistry, RAS, Moscow, Russia
                2KuB Ltd., Russia
                Author notes

                Academic Editor: Ming Chen

                Author information
                http://orcid.org/0000-0001-8527-6003
                Article
                DOI: 10.1155/2018/4625394
                PMC ID: 6083525
                SO-VID: ca1867d7-9632-416c-9028-a8927bf933b4
                Copyright © Copyright © 2018 Ekaterina Ilgisonis et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 4 March 2018
                Date accepted : 5 July 2018
                Funding
                Funded by: State Academies of Sciences Fundamental Scientific Research Program
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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