A novel gluten knowledge base of potential biomedical and health-related interactions extracted from the literature: Using machine learning and graph analysis methodologies to reconstruct the bibliome.

BACKGROUND: In return for their nutritional properties and broad availability, cereal crops have been associated with different alimentary disorders and symptoms, with the majority of the responsibility being attributed to gluten. Therefore, the research of gluten-related literature data continues to be produced at ever-growing rates, driven in part by the recent exploratory studies that link gluten to non-traditional diseases and the popularity of gluten-free diets, making it increasingly difficult to access and analyse practical and structured information. In this sense, the accelerated discovery of novel advances in diagnosis and treatment, as well as exploratory studies, produce a favourable scenario for disinformation and misinformation. OBJECTIVES: Aligned with, the European Union strategy "Delivering on EU Food Safety and Nutrition in 2050″ which emphasizes the inextricable links between imbalanced diets, the increased exposure to unreliable sources of information and misleading information, and the increased dependency on reliable sources of information; this paper presents GlutKNOIS, a public and interactive literature-based database that reconstructs and represents the experimental biomedical knowledge extracted from the gluten-related literature. The developed platform includes different external database knowledge, bibliometrics statistics and social media discussion to propose a novel and enhanced way to search, visualise and analyse potential biomedical and health-related interactions in relation to the gluten domain. METHODS: For this purpose, the presented study applies a semi-supervised curation workflow that combines natural language processing techniques, machine learning algorithms, ontology-based normalization and integration approaches, named entity recognition methods, and graph knowledge reconstruction methodologies to process, classify, represent and analyse the experimental findings contained in the literature, which is also complemented by data from the social discussion. RESULTS AND CONCLUSIONS: In this sense, 5814 documents were manually annotated and 7424 were fully automatically processed to reconstruct the first online gluten-related knowledge database of evidenced health-related interactions that produce health or metabolic changes based on the literature. In addition, the automatic processing of the literature combined with the knowledge representation methodologies proposed has the potential to assist in the revision and analysis of years of gluten research. The reconstructed knowledge base is public and accessible at https://sing-group.org/glutknois/.

MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions.

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

A framework to extract biomedical knowledge from gluten-related tweets: The case of dietary concerns in digital era.

Big data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from >400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.

Using Twitter to Understand the Human Bowel Disease Community: Exploratory Analysis of Key Topics.

BACKGROUND: Nowadays, the use of social media is part of daily life, with more and more people, including governments and health organizations, using at least one platform regularly. Social media enables users to interact among large groups of people that share the same interests and suffer the same afflictions. Notably, these channels promote the ability to find and share information about health and medical conditions. OBJECTIVE: This study aimed to characterize the bowel disease (BD) community on Twitter, in particular how patients understand, discuss, feel, and react to the condition. The main questions were as follows: Which are the main communities and most influential users?; Where are the main content providers from?; What are the key biomedical and scientific topics under discussion? How are topics interrelated in patient communications?; How do external events influence user activity?; What kind of external sources of information are being promoted? METHODS: To answer these questions, a dataset of tweets containing terms related to BD conditions was collected from February to August 2018, accounting for a total of 24,634 tweets from 13,295 different users. Tweet preprocessing entailed the extraction of textual contents, hyperlinks, hashtags, time, location, and user information. Missing and incomplete information about the user profiles was completed using different analysis techniques. Semantic tweet topic analysis was supported by a lexicon-based entity recognizer. Furthermore, sentiment analysis enabled a closer look into the opinions expressed in the tweets, namely, gaining a deeper understanding of patients' feelings and experiences. RESULTS: Health organizations received most of the communication, whereas BD patients and experts in bowel conditions and nutrition were among those tweeting the most. In general, the BD community was mainly discussing symptoms, BD-related diseases, and diet-based treatments. Diarrhea and constipation were the most commonly mentioned symptoms, and cancer, anxiety disorder, depression, and chronic inflammations were frequently part of BD-related tweets. Most patient tweets discussed the bad side of BD conditions and other related conditions, namely, depression, diarrhea, and fibromyalgia. In turn, gluten-free diets and probiotic supplements were often mentioned in patient tweets expressing positive emotions. However, for the most part, tweets containing mentions to foods and diets showed a similar distribution of negative and positive sentiments because the effects of certain food components (eg, fiber, iron, and magnesium) were perceived differently, depending on the state of the disease and other personal conditions of the patients. The benefits of medical cannabis for the treatment of different chronic diseases were also highlighted. CONCLUSIONS: This study evidences that Twitter is becoming an influential space for conversation about bowel conditions, namely, patient opinions about associated symptoms and treatments. So, further qualitative and quantitative content analyses hold the potential to support decision making among health-related stakeholders, including the planning of awareness campaigns.

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.

Application of agent-based modelling to assess single-molecule transport across the cell envelope of E. coli.

MOTIVATION: Single cells often show stochastic behaviour and variations in the physiological state of individual cells affect the behaviour observed in cell populations. This may be partially explained by variations in the concentration and spatial location of molecules within and in the vicinity of each cell. METHODS: This paper introduces an agent-based model that represents single-molecule transport through the cellular envelope of Escherichia coli at the micrometre scale. This model enables broader observation of molecular transport throughout the different membrane layers and the study of the effect of molecular concentration in cellular noise. Simulations considered various low molecular weight molecules, i.e. ampicillin, bosentan, coumarin, saquinavir, and terbutaline, and a gradient of molecular concentrations. The model ensured stochasticity in the location of the agents, using diffusing spherical particles with physical dimensions. RESULTS: Simulation results were validated against theoretical and experimental data. For example, theoretically, ampicillin molecules take 0.6 s to cross the entire cell envelope, and computational simulations took 0.68 s, 0.68 s, 0.70 s, and 0.69 s, for concentrations of 1.44 µM, 13.21 µM, 26.4 µM and 105.61 µM, respectively. Replicate standard deviation decreased with growing initial concentrations of the molecules. In turn, no clear relationship could be observed between molecular size and variability. CONCLUSIONS: This work presented a novel agent-based model to study the effect of the initial concentration of low molecular weight molecules on cellular noise. Cellular noise during molecule diffusion was found to be concentration-dependent and size-independent. The new model holds considerable potential for future, more complex analyses, when emerging experimental data may enable modelling of membrane transport mechanisms.

Exploring anti-quorum sensing and anti-virulence based strategies to fight Candida albicans infections: an in silico approach.

The complex virulence attributes of Candida albicans are an attractive target to exploit in the development of new antifungals and anti-virulence strategies to combat C. albicans infections. Particularly, quorum sensing (QS) has been reported as critical for virulence regulation in C. albicans. This work presents two knowledge networks with up-to-date information about QS regulation and experimentally tested anti-QS and anti-virulence agents for C. albicans. A semi-automatic bioinformatics workflow that combines literature mining and expert curation was used to retrieve otherwise scattered information from the scientific literature. The network representation offers an innovative and continuously updatable means for the Candida research community to query QS and virulence data systematically and in a user-friendly way. Notably, the reconstructed networks show the complexity of QS regulation and the impact that some molecules have on the inhibition of virulence mechanisms responsible for infection establishment (e.g. hyphal development) and perseverance (e.g. biofilm formation). In the future, the compiled knowledge may be used to build decision-making models that help infer new knowledge of practical significance. The knowledge networks are publicly available at http://pcquorum.org/. This Web platform enables the exploration of fungal virulence cues as well as reported inhibitors in a user-friendly fashion.

Agent-based model of diffusion of N-acyl homoserine lactones in a multicellular environment of Pseudomonas aeruginosa and Candida albicans.

Experimental incapacity to track microbe-microbe interactions in structures like biofilms, and the complexity inherent to the mathematical modelling of those interactions, raises the need for feasible, alternative modelling approaches. This work proposes an agent-based representation of the diffusion of N-acyl homoserine lactones (AHL) in a multicellular environment formed by Pseudomonas aeruginosa and Candida albicans. Depending on the spatial location, C. albicans cells were variably exposed to AHLs, an observation that might help explain why phenotypic switching of individual cells in biofilms occurred at different time points. The simulation and algebraic results were similar for simpler scenarios, although some statistical differences could be observed (p < 0.05). The model was also successfully applied to a more complex scenario representing a small multicellular environment containing C. albicans and P. aeruginosa cells encased in a 3-D matrix. Further development of this model may help create a predictive tool to depict biofilm heterogeneity at the single-cell level.

Collaborative relation annotation and quality analysis in Markyt environment.

Database URL: http://markyt.org.

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

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 identify missing and less studied links and to generate new research hypotheses.

Quorum sensing inhibition in Pseudomonas aeruginosa biofilms: new insights through network mining.

Quorum sensing plays a pivotal role in Pseudomonas aeruginosa's virulence. This paper reviews experimental results on antimicrobial strategies based on quorum sensing inhibition and discusses current targets in the regulatory network that determines P. aeruginosa biofilm formation and virulence. A bioinformatics framework combining literature mining with information from biomedical ontologies and curated databases was used to create a knowledge network of potential anti-quorum sensing agents for P. aeruginosa. A total of 110 scientific articles, corresponding to 1,004 annotations, were so far included in the network and are analysed in this work. Information on the most studied agents, QS targets and methods is detailed. This knowledge network offers a unique view of existing strategies for quorum sensing inhibition and their main regulatory targets and may be used to readily access otherwise scattered information and to help generate new testable hypotheses. This knowledge network is publicly available at http://pcquorum.org/ .

Construction of antimicrobial peptide-drug combination networks from scientific literature based on a semi-automated curation workflow.

Considerable research efforts are being invested in the development of novel antimicrobial therapies effective against the growing number of multi-drug resistant pathogens. Notably, the combination of different agents is increasingly explored as means to exploit and improve individual agent actions while minimizing microorganism resistance. Although there are several databases on antimicrobial agents, scientific literature is the primary source of information on experimental antimicrobial combination testing. This work presents a semi-automated database curation workflow that supports the mining of scientific literature and enables the reconstruction of recently documented antimicrobial combinations. Currently, the database contains data on antimicrobial combinations that have been experimentally tested against Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, Listeria monocytogenes and Candida albicans, which are prominent pathogenic organisms and are well-known for their wide and growing resistance to conventional antimicrobials. Researchers are able to explore the experimental results for a single organism or across organisms. Likewise, researchers may look into indirect network associations and identify new potential combinations to be tested. The database is available without charges.Database URL: http://sing.ei.uvigo.es/antimicrobialCombination/.

The Markyt visualisation, prediction and benchmark platform for chemical and gene entity recognition at BioCreative/CHEMDNER challenge.

Biomedical text mining methods and technologies have improved significantly in the last decade. Considerable efforts have been invested in understanding the main challenges of biomedical literature retrieval and extraction and proposing solutions to problems of practical interest. Most notably, community-oriented initiatives such as the BioCreative challenge have enabled controlled environments for the comparison of automatic systems while pursuing practical biomedical tasks. Under this scenario, the present work describes the Markyt Web-based document curation platform, which has been implemented to support the visualisation, prediction and benchmark of chemical and gene mention annotations at BioCreative/CHEMDNER challenge. Creating this platform is an important step for the systematic and public evaluation of automatic prediction systems and the reusability of the knowledge compiled for the challenge. Markyt was not only critical to support the manual annotation and annotation revision process but also facilitated the comparative visualisation of automated results against the manually generated Gold Standard annotations and comparative assessment of generated results. We expect that future biomedical text mining challenges and the text mining community may benefit from the Markyt platform to better explore and interpret annotations and improve automatic system predictions.Database URL: http://www.markyt.org, https://github.com/sing-group/Markyt.

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.

From amino acid sequence to bioactivity: The biomedical potential of antitumor peptides.

Chemoprevention is the use of natural and/or synthetic substances to block, reverse, or retard the process of carcinogenesis. In this field, the use of antitumor peptides is of interest as, (i) these molecules are small in size, (ii) they show good cell diffusion and permeability, (iii) they affect one or more specific molecular pathways involved in carcinogenesis, and (iv) they are not usually genotoxic. We have checked the Web of Science Database (23/11/2015) in order to collect papers reporting on bioactive peptide (1691 registers), which was further filtered searching terms such as "antiproliferative," "antitumoral," or "apoptosis" among others. Works reporting the amino acid sequence of an antiproliferative peptide were kept (60 registers), and this was complemented with the peptides included in CancerPPD, an extensive resource for antiproliferative peptides and proteins. Peptides were grouped according to one of the following mechanism of action: inhibition of cell migration, inhibition of tumor angiogenesis, antioxidative mechanisms, inhibition of gene transcription/cell proliferation, induction of apoptosis, disorganization of tubulin structure, cytotoxicity, or unknown mechanisms. The main mechanisms of action of those antiproliferative peptides with known amino acid sequences are presented and finally, their potential clinical usefulness and future challenges on their application is discussed.

Computational resources and strategies to construct single-molecule metabolic models of microbial cells.

Recent computational methodologies, such as individual-based modelling, pave the way to the search for explanatory insight into the collective behaviour of molecules. Many reviews offer an up-to-date perspective about such methodologies, but little is discussed about the practical information requirements involved. The biological information used as input should be easily and routinely determined in the laboratory, publicly available and, preferably, organized in programmatically accessible databases. This review is the first to provide a systematic and comprehensive overview of available resources for the modelling of metabolic events at the molecular scale. The glycolysis pathway of Escherichia coli, which is one of the most studied pathways in Microbiology, serves as case study. This curation addressed structural information about E. coli (i.e. defining the simulation environment), the reactions forming the glycolysis pathway including the enzymes and the metabolites (i.e. the molecules to be represented), the kinetics of each reaction (i.e. behavioural logic of the molecules) and diffusion parameters for all enzymes and metabolites (i.e. molecule movement in the environment). Furthermore, the interpretation of relevant biological features, such as molecular diffusion and enzyme kinetics, and the connection of experimental determination and simulation validation are detailed. Notably, the information from classical theories, such as enzymatic rates and diffusion coefficients, is translated to simulation parameters, such as collision efficiency and particle velocity.

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.

Marky: a tool supporting annotation consistency in multi-user and iterative document annotation projects.

BACKGROUND AND OBJECTIVES: Document annotation is a key task in the development of Text Mining methods and applications. High quality annotated corpora are invaluable, but their preparation requires a considerable amount of resources and time. Although the existing annotation tools offer good user interaction interfaces to domain experts, project management and quality control abilities are still limited. Therefore, the current work introduces Marky, a new Web-based document annotation tool equipped to manage multi-user and iterative projects, and to evaluate annotation quality throughout the project life cycle. METHODS: At the core, Marky is a Web application based on the open source CakePHP framework. User interface relies on HTML5 and CSS3 technologies. Rangy library assists in browser-independent implementation of common DOM range and selection tasks, and Ajax and JQuery technologies are used to enhance user-system interaction. RESULTS: Marky grants solid management of inter- and intra-annotator work. Most notably, its annotation tracking system supports systematic and on-demand agreement analysis and annotation amendment. Each annotator may work over documents as usual, but all the annotations made are saved by the tracking system and may be further compared. So, the project administrator is able to evaluate annotation consistency among annotators and across rounds of annotation, while annotators are able to reject or amend subsets of annotations made in previous rounds. As a side effect, the tracking system minimises resource and time consumption. CONCLUSIONS: Marky is a novel environment for managing multi-user and iterative document annotation projects. Compared to other tools, Marky offers a similar visually intuitive annotation experience while providing unique means to minimise annotation effort and enforce annotation quality, and therefore corpus consistency. Marky is freely available for non-commercial use at http://sing.ei.uvigo.es/marky.