Selected Extended Papers of the 11th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB)

[Excerpt] This special issue includes extended versions of a number of papers selected from the International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB 2017) that was held in Porto (Portugal)inJune2017.Thisforum,alreadyinitseleventhedition,aimstogatherandpromotetheinteraction of a community of researchers developing applied Bioinformatics or Chemoinformatics solutions for diverse problemsinbiologicalandbiomedicalresearch. [...]info:eu-repo/semantics/publishedVersio

Advanced Practical Applications of Computational Biology & Bioinformatics: PACBB15

[Excerpt] The success of Bioinformatics in recent years has been prompted by research in Molecular Biology and Molecular Medicine in several initiatives. These initiatives gave rise to an exponential increase in the volume and diversification of data, including next generation sequencing data and their annotations, high-throughput experimental (omics) data, biomedical literature, among many others. Systems Biology is a related research area that has been replacing the reductionist” view that dominated Biology research in the last decades, requiring the coordinated efforts of biological researchers with those related to data analysis, mathematical modeling, computer simulation and optimization. [...]info:eu-repo/semantics/publishedVersio

Moodle and affective computing : knowing who´s on the other side

In traditional learning, teachers can easily get an insight into how their students work and learn, and how they interact in the classroom. However, in online learning, it is more difficult for teachers to see how individual students behave and learn, and very important, their mood to do it. Student’s emotions like self-esteem, motivation, commitment, and others that are believed to be determinant in student’s performance can not be ignored, as they are known (affective states and also learning styles) to greatly influence student´s learning. This paper deals with the student’s behavioural and affective aspects in virtual learning environments to enhance the students’ learning, gain and experience. The goal is to achieve a similar performance to a skilled teacher that can modify the learning path and his teaching style according to the feedback signals provided by the students - which include cognitive, emotional and motivational aspects. This can be done through the recognition of students actual mood, and we propose a framework to implement and address such issues in Moodle

BIOMedical search engine framework: lightweight and customized implementation of domain-specific biomedical search engines

The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsefBackground and Objectives: Text mining and semantic analysis approaches can be applied to the construction of biomedical domain-specific search engines and provide an attractive alternative to create personalized and enhanced search experiences. Therefore, this work introduces the new open-source BIOMedical Search Engine Framework for the fast and lightweight development of domain-specific search engines. The rationale behind this framework is to incorporate core features typically available in search engine frameworks with flexible and extensible technologies to retrieve biomedical documents, annotate meaningful domain concepts, and develop highly customized Web search interfaces. Methods: The BIOMedical Search Engine Framework integrates taggers for major biomedical concepts, such as diseases, drugs, genes, proteins, compounds and organisms, and enables the use of domain-specific controlled vocabulary. Technologies from the Typesafe Reactive Platform, the AngularJS JavaScript framework and the Bootstrap HTML/CSS framework support the customization of the domain-oriented search application. Moreover, the RESTful API of the BIOMedical Search Engine Framework allows the integration of the search engine into existing systems or a complete web interface personalization. Results The construction of the Smart Drug Search is described as proof-of-concept of the BIOMedical Search Engine Framework. This public search engine catalogs scientific literature about antimicrobial resistance, microbial virulence and topics alike. The keyword-based queries of the users are transformed into concepts and search results are presented and ranked accordingly. The semantic graph view portraits all the concepts found in the results and the researcher may look into the relevance of different concepts, the strength of direct relations, and non-trivial, indirect relations. The number of occurrences of the concept shows its importance to the query, and the frequency of concept co-occurrence is indicative of biological relations meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations. Conclusions The BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibility in terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts.This work was partially funded by the [14VI05] ContractProgramme from the University ofVigo and theAgrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa(2012/273).The research leading to these results has also received funding from the European Union Seventh Framework Programme FP7/REGPOT-2012-2013.1 under grant agreement n° 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 herei

Behavior analysis enviroments e-learning

La evaluación representa un aspecto determinante en la elaboración de estrategias de éxito durante el aprendizaje. En un contexto presencial, el docente puede observar el comportamiento de sus alumnos e identificar diferentes vías que faciliten la evaluación sin inducir al estrés, evitando las consecuencias negativas de éste en el resultado del aprendizaje. Sin embargo, en entornos de aprendizaje con e-Learning el contacto directo resulta imposible y, por lo tanto, deben existir formas alternativas que faciliten tanto la detección como la prevención de situaciones de estrés durante la evaluación. Resulta por tanto conveniente el análisis del estrés y la determinación de estrategias para la resolución de problemas derivados de su aparición. En este trabajo se propone un módulo de análisis de estrés para su aplicación durante la evaluación on-line de los alumnos, que es capaz de indicar al docente los instantes de tiempo más propicios para intervenir así como los contenidos que causan mayores dificultades. De esta forma, el educador podrá asistir de forma eficaz a aquellos alumnos que más lo necesiten.The evaluation is a determining factor in developing successful strategies for learning. In a classroom context, the teacher can observe the behavior of students and identify different ways to facilitate the assessment without inducing stress, avoiding the negative consequences of this on the result of learning. However, in learning environments eLearning direct contact is impossible and, therefore, there should be alternative ways to provide both detection and prevention of stress during the evaluation. It is therefore appropriate stress analysis and identification of strategies for solving problems arising from its appearance. In this work, a stress analysis module for use is proposed for the online student assessment, which is capable of indicating to the teaching moments more propitious time to intervene and the contents that cause greater difficulties. In this way, the teacher can effectively assist students who need it most

HaemoKBS: a knowledge-based system for real-time, continuous categorisation of adverse reactions in blood recipients

This work introduces HaemoKBS, a novel Haemovigilance decision support system for adverse reactions in blood recipients. Machine learning inference and rule-based reasoning were applied to build the underlying decision support models, namely to automatically extract evidence from different types of data included in hospital notifications and incorporate a priori expert knowledge. The ultimate aim is to dynamically learn and improve the reasoning abilities of the system and thus, be able to provide educated recommendations to hospital notifiers along with understandable explanations on the acquired knowledge. Experiments over the records of the Portuguese National Haemovigilance System from the last 10 years demonstrate the practical usefulness of HaemoKBS, which will contribute to a better depiction of the adverse reactions and to flag any incomplete notification enforcing data quality.SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from the University of Vigo for hosting its IT infrastructure. This work was partially supported by the 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 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).info:eu-repo/semantics/publishedVersio

E-learning platforms and e-learning students : building the bridge to success

E-learning platforms are becoming more and more common in education and with organisations. They are seen as a complementary tool to support learning or, as in many cases, as the primary tool to do it (possibly the only one). In traditional learning, teachers can easily get an insight into how their students work and learn, and how they interact in the classroom. However, in online learning, it is more difficult for teachers to see how individual students behave. Affective states and learning styles are determinant in students’ performance. Together with stress, these are crucial factor to success. It is believed that the sole use of an E-learning platform can in itself be a cause of stress for students. Estimating, in a non-invasive way, such parameters, and taking measures to deal with them, are then the goal of this paper. We do not consider the use of dedicated sensors (invasive) such as special gloves or wrist bracelets since we intend not to be dependent on specific hardware and also because we believe that such specific hardware can induce for itself some alteration in the parameters being analysed. Our work focuses on the development of a new module (Dynamic Recognition Module) to incorporate in Moodle E-learning platform, to accommodate individualized support to E-learning students

Hospitality and Hospital Management

A hospital is a health organization that is largely run by doctors and nurses. Hospitality is the operational facet that helps people feel welcome, relaxed and comfortable. Hospitals are indeed examples of forced hospitality, their patients need a bed, they need to eat, but the element of choice and positive desire is crucial here, making the way to a creative and responsible Hospitality and Hospital Management. At this point, an eye is cast on Social Media (SM) and Social Networking (SNet) and how they can have a say in order to achieve this goal. This work was carried out as a case study. A total of 56 physicians, nurses and patients participated in this study by answering questionnaires. The ages of the contributor’s range d from 25 to 56 years old (mean age 37 years old), with 60% women and 40% men. The questionnaire consists of two sections, where the former one contains general questions (e.g. age, gender, academic qualifications), while the second includes information on dimensions such as Information Acquisition, Innovative Culture, Financial Performance, Food Quality and Staff-Services Issues, which are in line with the objectives of this work. Mathematical Logical Programs are presented that take into account the awareness of physicians, nurses and patients about their level of satisfaction with Hospitality and Hospital Management, in terms of an accurate description of their peculiar entropic states, leading to speed of decision-making, strategic focus, the ability to pursue long-term goals, as well as closer proximity among physicians, nurses and patients. This work shows that it is possible to predict trends in Hospitality and Hospital Management, therefore enabling preventive/corrective actions that can help create the best conditions for the physicians and nurse’s professional development, leading to patient satisfaction. On the other hand, the classic framework in communication has developed into Theoretical and Model-based approaches to Problem Solving, owing the Theoretical one a clear advantage over the Model-based, once proof theory yields a precise framework in which to articulate the computational features of the Logic Programming language with the internal behavior of such systems, set in terms of their variables Entropic States and of an intetwining of SM with SNet

P4P: a peptidome-based strain-level genome comparison web tool

Peptidome similarity analysis enables researchers to gain insights into differential peptide profiles, providing a robust tool to discriminate strain-specific peptides, true intra-species differences among biological replicates or even microorganism-phenotype variations. However, no in silico peptide fingerprinting software existed to facilitate such phylogeny inference. Hence, we developed the Peptidomes for Phylogenies (P4P) web tool, which enables the survey of similarities between microbial proteomes and simplifies the process of obtaining new biological insights into their phylogeny. P4P can be used to analyze different peptide datasets, i.e. bacteria, viruses, eukaryotic species or even metaproteomes. Also, it is able to work with whole proteome datasets and experimental mass-to-charge lists originated from mass spectrometers. The ultimate aim is to generate a valid and manageable list of peptides that have phylogenetic signal and are potentially sample-specific. Sample-to-sample comparison is based on a consensus peak set matrix, which can be further submitted to phylogenetic analysis. P4P holds great potential for improving phylogenetic analyses in challenging taxonomic groups, biomarker identification or epidemiologic studies. Notably, P4P can be of interest for applications handling large proteomic datasets, which it is able to reduce to small matrices while maintaining high phylogenetic resolution. The web server is available at http://sing-group.org/p4p.Spanish ‘Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad’ [AGL2013-44039R]; Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020[POCI-01-0145-FEDER-006684];INOU16-05project from the University of Vigo; Fundación AECC. Funding for open access charge: Spanish ‘Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad’ [AGL2013-44039R].info:eu-repo/semantics/publishedVersio

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

Journal pre proofBig 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 more than 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.SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its IT infrastructure. This work was supported by: the Associate Laboratory for Green Chemistry-LAQV, which is financed by national funds from and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of [UIDB/50006/2020] and [UIDB/04469/2020] units, and BioTecNorte operation [NORTE010145FEDER000004] funded by the European Regional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte, the Xunta de Galicia (Centro singular de investigacion de Galicia accreditation 2019-2022) and the European Union (European Regional Development Fund - ERDF)- Ref. [ED431G2019/06] , and Conselleria de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) under the scope of the strategic funding of [ED431C2018/55GRC] Competitive Reference Group. The authors also acknowledge the post-doctoral fellowship [ED481B2019032] of Martin PerezPerez, funded by the Xunta de Galicia. Funding for open access charge: Universidade de Vigo/CISUGinfo:eu-repo/semantics/publishedVersio