Online visibility of software-related web sites: The case of biomedical text mining tools

Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.ipm.2018.11.011.Internet, in general, and the WWW, in particular, have become an immediate, practical means of introducing software tools and resources, and most importantly, a key vehicle to attract the attention of the potential users. In this scenario, content organization as well as different development practices may affect the online visibility of the target resource. Therefore, the careful selection, organization and presentation of contents are critical to guarantee that the main features of the target tool can be easily discovered by potential visitors, while ensuring a proper indexation by automatic online systems and resource recognizers. Understanding how software is depicted in scientific manuscripts and comparing these texts with the corresponding online descriptions can help to improve the visibility of the target website. It is particularly relevant to be able to align online descriptions and those found in literature, and use the resulting knowledge to improve software indexing and grouping. Therefore, this paper presents a novel method for formally defining and mining software-related websites and related literature with the ultimate aim of improving the global online visibility of the software. As a proof of concept, the method was used to evaluate the online visibility of biomedical text mining tools. These tools have evolved considerably in the last decades, and are gathering together a heterogeneous development community as well as various user groups. For the most part, these tools are not easily discovered via general search engines. Hence, the proposed method enabled the identification of specific issues regarding the visibility of these online contents and the discussion of some possible improvements.SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure. This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE2020(POCI-01-0145-FEDER-006684).The authors also acknowledge the Ph.D.grants of MartínPérez-Pérez and Gael Pérez - Rodríguez, funded by the Xunta de Galicia.info:eu-repo/semantics/publishedVersio

Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.future.2020.04.025.Supplementary material 1: this file contains the 23 user communities detected using the GLay algorithm.In recent years, the number of active users in social media has grown exponentially. Despite the thematic diversity of the messages, social media have become an important vehicle to disseminate health information as well as to gather insights about patients experiences and emotional intelligence. Therefore, the present work proposes a new methodology of analysis to identify and interpret the behaviour, perceptions and appreciations of patients and close relatives towards a health condition through their social interactions. At the core of this methodology are techniques of natural language processing and machine learning as well as the reconstruction of knowledge graphs, and further graph mining. The case study is the diabetes community, and more specifically, the patients communicating about type 1 diabetes (T1D) and type 2 diabetes (T2D). The results produced in this study show the effectiveness of the proposed method to discover useful and non-trivial knowledge about patient perceptions of disease. Such knowledge may be used in the context of Health Informatics to promote healthy lifestyles in more efficient ways as well as to improve communication with the patients.This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684), the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019–2022) and the European Union (European Regional Development Fund - ERDF)- Ref. ED431G2019/06, and Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group. The authors also acknowledge the Postdoc contract of Martín Pérez-Pérez, funded by the Xunta de Galicia.info:eu-repo/semantics/publishedVersio

A network perspective on antimicrobial peptide combination therapies: the potential of colistin, polymyxin B and nisin

Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ijantimicag.2017.02.012.Antimicrobial combinations involving antimicrobial peptides (AMPs) attract considerable attention within current antimicrobial and anti-resistance research. The objective of this study was to review the available scientific literature on the effects of antimicrobial combinations involving colistin (polymyxin E), polymyxin B and nisin, which are US Food and Drug Administration (FDA)-approved AMPs broadly tested against prominent multidrug-resistant pathogens. A bioinformatics approach based on literature mining and manual expert curation supported the reconstruction of experimental evidence on the potential of these AMP combinations, as described in the literature. Network analysis enabled further characterisation of the retrieved antimicrobial agents, targets and combinatory effects. This systematic analysis was able to output valuable information on the studies conducted on colistin, polymyxin B and nisin combinations. The reconstructed networks enable the traversal and browsing of a large number of agent combinations, providing comprehensive details on the organisms, modes of growth and methodologies used in the studies. Therefore, network analysis enables a bird's-eye view of current research trends as well as in-depth analysis of specific drugs, organisms and combinatory effects, according to particular user interests. The reconstructed knowledge networks are publicly accessible at http://sing-group.org/antimicrobialCombination/. Hopefully, this resource will help researchers to look into antimicrobial combinations more easily and systematically. User-customised queries may help to identify missing and less studied links and to generate new research hypotheses.This work was supported by the Portuguese Foundation for Science and Technology(FCT)under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 [POCI01-0145-FEDER-006684] and BioTecNorte operation [NORTE-010145-FEDER-000004], funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte.The authors also acknowledge the support received from FCT and the European Community fund FEDER, through Program COMPETE, under the scope of the Project RECI/BBB-EBI/0179/2012 [FCOMP-01-0124-FEDER-027462],the[14VI05]Contract-Programme from the University of Vigo (Vigo, Spain), the INOU-16-05 project from the Provincial Council of Ourense, and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa [2012/273]. SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure. Finally, the authors acknowledge the PhD grant of Paula Jorge[Grant no. SFRH/BD/88192/2012],funded by FCT,thePhD grants of Martín Pérez-Pérez and Gael Pérez-Rodríguez, funded by the Xunta de Galicia and the University of Vigo, and the Research grant 2014 of Anália Lourenço by the European Society of Clinical Microbiology and Infectious Diseases (ESCMID).info:eu-repo/semantics/publishedVersio

Single molecule simulation of diffusion and enzyme kinetics

This work presents a molecular-scale agent-based model for the simulation of enzymatic reactions at experimentally measured concentrations. The model incorporates stochasticity and spatial dependence, using diffusing and reacting particles with physical dimensions. We developed strategies to adjust and validate the enzymatic rates and diffusion coefficients to the information required by the computational agents, i.e., collision efficiency, interaction logic between agents, the time scale associated with interactions (e.g., kinetics), and agent velocity. Also, we tested the impact of molecular location (a source of biological noise) in the speed at which the reactions take place. Simulations were conducted for experimental data on the 2-hydroxymuconate tautomerase (EC 5.3.2.6, UniProt ID Q01468) and the Steroid Delta-isomerase (EC 5.3.3.1, UniProt ID P07445). Obtained results demonstrate that our approach is in accordance to existing experimental data and long-term biophysical and biochemical assumptions.This work was financially supported by Project UID/EQU/ 00511/2013-LEPABE, by the FCT/MEC with national funds and when applicable cofunded by FEDER in the scope of the P2020 Partnership Agreement; Project NORTE-07-0124FEDER-000025 - RL2 Environment&Health, by FEDER funds through Programa Operacional Factores de Competitividade - COMPETE, by the Programa Operacional do Norte (ON2) program and by national funds through FCT Fundação para a Ciência e a Tecnologia. This work was also partially funded by the [14VI05] Contract-Programme from the University of Vigo and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/ 273). The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP7/REGPOT-2012-2013.1 under Grant Agreement No. 316265, BIOCAPS. This document reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained herein

Agent-based spatiotemporal simulation of biomolecular systems within the open source MASON framework

Agent-based modelling is being used to represent biological systems with increasing frequency and success. This paper presents the implementation of a new tool for biomolecular reaction modelling in the open source Multiagent Simulator of Neighborhoods framework. The rationale behind this new tool is the necessity to describe interactions at the molecular level to be able to grasp emergent and meaningful biological behaviour. We are particularly interested in characterising and quantifying the various effects that facilitate biocatalysis. Enzymes may display high specificity for their substrates and this information is crucial to the engineering and optimisation of bioprocesses. Simulation results demonstrate that molecule distributions, reaction rate parameters, and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of realistic cell environments. While higher percentage of collisions with occurrence of reaction increases the affinity of the enzyme to the substrate, a faster reaction (i.e., turnover number) leads to a smaller number of time steps. Slower diffusion rates and molecular crowding (physical hurdles) decrease the collision rate of reactants, hence reducing the reaction rate, as expected. Also, the random distribution of molecules affects the results significantly.The authors thank the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273). The research leading to these results has received funding from the European Union's Seventh Framework Programme FP7/REGPOT-2012-2013.1 under Grant Agreement no. 316265 (BIOCAPS) and the [14VI05] Contract-Programme from the University of Vigo. This document reflects only the authors' views and the European Union is not liable for any use that may be made of the information contained herein

Developing timely insights into Pseudomonas aeruginosa quorum sensing therapeutics through text mining

Publicado em "Biofilms7: microbial works of art: book of abstracts". ISBN 978-989-97478-7-6The pervasive growth of antibiotic-resistant is pressing the development of novel strategies to control infectious diseases. Quorum sensing (QS) is a key communication mechanism that allows bacteria to regulate gene expression, and thus many physiological activities e.g. virulence, motility, and biofilm formation. Hence, QS inhibition or quorum quenching is being pursued as a promising strategy to control clinical pathogens.Most available information about drug interactions with QS genes and molecules is scattered in the vast and ever-growing biomedical bibliome. So, text mining and network mining are attractive solutions to identify relevant interactions and generate new hypothesis for antimicrobial research.Here, we describe the implementation of such an automated workflow that extracts key information on P. aeruginosa QS-focused antimicrobial strategies from PubMed records. The workflow produces an integrated network, capturing the effect of antimicrobial agents over QS genes, QS signals and virulence factors. Interactions are contextualised by information on the conducted experimental methods and details on the antimicrobials and QS entities retrieved. The public Web-based interface (http://pcquorum.org) enables users to navigate through the interactions and look for indirect, non-trivial antimicrobial-QS associations.Currently, the P. aeruginosa antimicrobial-QS network contains 439 interactions encompassing 170 different drugs and 72 different QS entities; but it is in continuous, semi-automated growth. It offers a comprehensive picture of emerging anti-QS findings and thus may help in gaining novel understanding and prioritising new antimicrobial experiments

Benchmarking biomedical text mining web servers at BioCreative V.5: the technical Interoperability and Performance of annotation Servers - TIPS track

The TIPS track consisted in a novel experimental task under the umbrella of the BioCreative text mining challenges with the aim to, for the first time ever, carry out a text mining challenge with particular focus on the continuous assessment of technical aspects of text annotation web servers, specifically of biomedical online named entity recognition systems. A total of 13 teams registered annotation servers, implemented in various programming languages, supporting up to 12 different general annotation types. The continuous evaluation period took place from February to March 2017. The systematic and continuous evaluation of server responses accounted for testing periods of low activity and moderate to high activity. Moreover three document provider settings were covered, including also NCBI PubMed. For a total of 4,092,502 requests, the median response time for most servers was below 3.74 s with a median of 10 annotations/document. Most of the servers showed great reliability and stability, being able to process 100,000 requests in 5 days.info:eu-repo/semantics/publishedVersio

Use social media knowledge for exploring the portuguese wine industry: following talks and perceptions?

This work presents an exploratory study that retrieves, processes, and analyses Twitter data to gain insights about the relevance and perceptions of the wine industry in the Douro Portuguese region (including Porto and Douro wines), as well as other regions in the country. The main techniques and algorithms used in our work belong to the families of natural language processing and machine learning, and the practical relevance of the proposed methodology has been proven in the analysis of 1.2 million unique messages from more than 764,000 distinct users retrieved from the Twitter platform. Derived results from this study are valuable to provide insights that can be further used in the context of Business Informatics to promote better and more efficient marketing campaigns, for example, centering the topic on the most interested people or communicating with the most appropriate words.,is work was supported by the Associate Laboratory for Green Chemistry—LAQV, financed by the Portuguese Foundation for Science and Technology (FCT/MCTES) Ref. UID/QUI/50006/2020; the Portuguese Foundation for Sci ence and Technology (FCT/MCTES) under the scope of the strategic funding of UIDB/04469/2020 unit and Bio TecNorte operation funded by the European Regional De velopment Fund (ERDF) under the scope of Norte2020—Programa Operacional Regional do Norte. Ref. NORTE-01-0145-FEDER-000004; the Conseller´ıa de Edu cacion, Universidades e Formaci ´ on Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, the “Centro singular de investigacion de Galicia” (accreditation 2019-2022) funded by the European Regional Development Fund (ERDF)-Ref. ED431G2019/06; and Portuguese Foundation for Science and Technology for a PhD Grant (SFRH/BD/145497/2019).info:eu-repo/semantics/publishedVersio

Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm

Background: Shared tasks and community challenges represent key instruments to promote research, collaboration and determine the state of the art of biomedical and chemical text mining technologies. Traditionally, such tasks relied on the comparison of automatically generated results against a so-called Gold Standard dataset of manually labelled textual data, regardless of efficiency and robustness of the underlying implementations. Due to the rapid growth of unstructured data collections, including patent databases and particularly the scientific literature, there is a pressing need to generate, assess and expose robust big data text mining solutions to semantically enrich documents in real time. To address this pressing need, a novel track called “Technical interoperability and performance of annotation servers” was launched under the umbrella of the BioCreative text mining evaluation effort. The aim of this track was to enable the continuous assessment of technical aspects of text annotation web servers, specifically of online biomedical named entity recognition systems of interest for medicinal chemistry applications. Results: A total of 15 out of 26 registered teams successfully implemented online annotation servers. They returned predictions during a two-month period in predefined formats and were evaluated through the BeCalm evaluation platform, specifically developed for this track. The track encompassed three levels of evaluation, i.e. data format considerations, technical metrics and functional specifications. Participating annotation servers were implemented in seven different programming languages and covered 12 general entity types. The continuous evaluation of server responses accounted for testing periods of low activity and moderate to high activity, encompassing overall 4,092,502 requests from three different document provider settings. The median response time was below 3.74 s, with a median of 10 annotations/document. Most of the servers showed great reliability and stability, being able to process over 100,000 requests in a 5-day period. Conclusions: The presented track was a novel experimental task that systematically evaluated the technical performance aspects of online entity recognition systems. It raised the interest of a significant number of participants. Future editions of the competition will address the ability to process documents in bulk as well as to annotate full-text documents.Portuguese Foundation for Science and Technology | Ref. UID/BIO/04469/2013Portuguese Foundation for Science and Technology | Ref. COMPETE 2020 (POCI-01-0145-FEDER-006684)Xunta de Galicia | Ref. ED431C2018/55-GRCEuropean Commission | Ref. H2020, n. 65402

Evaluation of chemical and gene/protein entity recognition systems at BioCreative V.5: the CEMP and GPRO patents tracks

This paper presents the results of the BioCreative V.5 offline tasks related to the evaluation of the performance as well as assess progress made by strategies used for the automatic recognition of mentions of chemical names and gene in running text of medicinal chemistry patent abstracts. A total of 21 teams submitted results for at least one of these tasks. The CEMP (chemical entity mention in patents) task entailed the detection of chemical named entity mentions. A total of 14 teams submitted 56 runs. The top performing team reached an F-score of 0.90 with a precision of 0.88 and a recall of 0.93. The GPRO (gene and protein related object) task focused on the detection of mentions of gene and protein related objects. The 7 participating teams (30 runs) had to detect gene/protein mentions that could be linked to at least one biological database, such as SwissProt or EntrezGene. The best F-score, recall and precision in this task were of 0.79, 0.83 and 0.77, respectively. The CEMP and GPRO gold standard corpora included training sets of 21,000 records and test sets of 9,000 records. Similar to the previous BioCreative CHEMDNER tasks, evaluation was based on micro-averaged F-score. The BeCalm platform supported prediction submission and evaluation (http://www.becalm.eu).We acknowledge the OpenMinted (654021) and the ELIXIREXCELERATE (676559) H2020 projects, and the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology for funding. The Spanish National Bioinformatics Institute (INB) unit at the Spanish National Cancer Research Centre (CNIO) is a member of the INB, PRB2-ISCIII and is supported by grant PT13/0001/0030, of the PE I+D+i 2013-2016, funded by ISCIII and ERDF.info:eu-repo/semantics/publishedVersio