Search

"Available online 11 November 2021", The increasing number of scientific research documents published keeps growing at an unprecedented rate, making it increasingly difficult to access practical information within a target domain. This situation is motivating a growing interest in applying text mining techniques for the automatic processing of text resources to structure the information that helps researchers to find information of interest and infer knowledge of practical use. However, the automatic processing of research documents requires the previous existence of large, manually annotated text corpora to develop robust and accurate text mining processing methods and machine learning models. In this context, semi-automatic extraction techniques based on structured data and state-of-the-art biomedical tools appear to have significant potential to enhance curator productivity and reduce the costs of document curation. In this line, this work proposes a semi-automatic machine learning workflow and a NER+Ontology boosting technique for the automatic classification of biomedical literature. The practical relevance of the proposed approach has been proven in the curation of 4,115 gluten-related documents extracted from PubMed and contrasted against the word embedding alternative. Comparing the results of the experiments, the proposed NER+Ontology technique is an effective alternative to other state-of-the-art document representation techniques to process the existing biomedical literature., This 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 Science and Technology (FCT/MCTES) under the scope of the strategic funding of UIDB/04469/2020 unit and BioTecNorte operation funded by the European Regional Development Fund (ERDF) under the scope of Norte2020— Programa Operacional Regional do Norte. Ref. NORTE-01-0145-FEDER-000004; 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 “Centro singular de investigación de Galicia” (accreditation 2019-2022) funded by the European Regional Development Fund (ERDF)-Ref. ED431G2019/06. The authors also acknowledge the postdoctoral fellowship [ED481B-2019-032] of Martín Pérez-Pérez, funded by Xunta de Galicia., info:eu-repo/semantics/publishedVersion

Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guidelines non-adherence have been proved to be associated with higher mortality. Machine Learning is increasingly being adopted in developing innovative Clinical Decision Support Systems in many areas of medicine, showing a great potential for automatic prediction of diverse patient conditions, as well as assistance in clinical decision making. In this context, this work conducts a narrative review to provide an overview of how specific Machine Learning techniques can be used to improve sepsis management, discussing the main tasks addressed, the most popular methods and techniques, as well as the obtained results, in terms of both intelligent system accuracy and clinical outcomes improvement.

Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current “smart everything” scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the design and development of specific mechanisms for enabling a collaborative inference across edge devices towards the in situ execution of highly complex state-of-the-art deep neural networks (DNNs), despite the resource-constrained nature of such infrastructures. In particular, the review discusses the most salient approaches conceived along those lines, elaborating on the specificities of the partitioning schemes and the parallelism paradigms explored, providing an organized and schematic discussion of the underlying workflows and associated communication patterns, as well as the architectural aspects of the DNNs that have driven the design of such techniques, while also highlighting both the primary challenges encountered at the design and operational levels and the specific adjustments or enhancements explored in response to them. Agencia Estatal de Investigación | Ref. DPI2017-87494-R Ministerio de Ciencia e Innovación | Ref. PDC2021-121644-I00 Xunta de Galicia | Ref. ED431C 2022/03-GRC

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG Discover relevant biomedical interactions in the literature is crucial for enhancing biology research. This curation process has an essential role in studying the different processes and interactions reported that affect the biological process (e.g., genome, metabolome, and transcriptome). In this sense, the objective of this work is twofold: reduce the manual effort required to curate and review the existing biochemical interactions reported in the gluten-related bibliome, while proposing a novel vector-space integrated into a deep learning model to assists manual curators in a real curation task by learning from their previous decisions. With this objective, the present work proposes a novel vector-space that combine (i) high-level lexical and syntactic inference features as Wordnets and Health-related domain ontologies, (ii) unsupervised semantic resources as word embedding, (iii) semantic and syntactic sentence knowledge, (iv) abbreviation resolution support, (v) several state-of-the-art Named-entity recognition methods, and, finally, (vi) different feature construction and optimization techniques to support a semi-automatic curation workflow. Therefore, the application of the proposed workflow over a classified set of 2,451 relevant gluten-related documents produces a total of 8,349 relevant and 471,813 irrelevant relations distributed in thirteen domain health-related categories. Experimental results showed that the proposed workflow is a valuable approach for a semi-automatic relation extraction task. It was able to obtain satisfactory results in the early stages of a real-world curation task and saved manual annotation efforts by learning from the decisions made by manual curators in iterative annotation rounds. The average F.score for the proposed relation categories was 0.731, being the lowest F.score at 0.47 and the highest F.score at 0.929. The different resources used in this work as well as the manually curated corpus are public available on our GitHub repository. Fundação para a Ciência e a Tecnologia | Ref. UIDB/50006/2020 Xunta de Galicia | Ref. ED431C2018/55 Xunta de Galicia | Ref. ED431G2019/06 Xunta de Galicia | Ref. ED481B-2019-032

The identification of clinically relevant bacterial amino acid changes can be performed using different methods aimed at the identification of genes showing positively selected amino acid sites (PSS). Nevertheless, such analyses are time consuming, and the frequency of genes showing evidence for PSS can be low. Therefore, the development of a pipeline that allows the quick and efficient identification of the set of genes that show PSS is of interest. Here, we present Auto-PSS-Genome, a Compi-based pipeline distributed as a Docker image, that automates the process of identifying genes that show PSS using three different methods, namely codeML, FUBAR, and omegaMap. Auto-PSS-Genome accepts as input a set of FASTA files, one per genome, containing all coding sequences, thus minimizing the work needed to conduct positively selected sites analyses. The Auto-PSS-Genome pipeline identifies orthologous gene sets and corrects for multiple possible problems in input FASTA files that may prevent the automated identification of genes showing PSS. A FASTA file containing all coding sequences can also be given as an external global reference, thus easing the comparison of results across species, when gene names are different. In this work, we use Auto-PSS-Genome to analyse Mycobacterium leprae (that causes leprosy), and the closely related species M. haemophilum, that mainly causes ulcerating skin infections and arthritis in persons who are severely immunocompromised, and in children causes cervical and perihilar lymphadenitis. The genes identified in these two species as showing PSS may be those that are partially responsible for virulence and resistance to drugs.

Deep Learning (DL) has attracted a lot of attention in the field of medical image analysis because of its higher performance in image classification when compared to previous state-of-the-art techniques. In addition, a recent meta-analysis found that the diagnostic performance of DL models is equivalent to that of health-care professionals. In this scenario, a lot of research using DL for polyp detection and classification have been published showing promising results in the last five years. Our work aims to review the most relevant studies from a technical point of view, focusing on the low-level details for the implementation of the DL models. To do so, this review analyzes the published research covering aspects like DL architectures, training strategies, data augmentation, transfer learning, or the features of the datasets used and their impact on the performance of the models. Additionally, comparative tables summarizing the main aspects analyzed in this review are publicly available at https://github.com/sing-group/deep-learning-colonoscopy. Xunta de Galicia | Ref. ED431C2018 / 55-GRC Ministerio de Economía, Industria y Competitividad | Ref. DPI2017-87494-R Xunta de Galicia | Ref. ED481A-2019/299 Xunta de Galicia | Ref. ED481B 2016 / 068-0 Instituto de Salud Carlos III | Ref. PI11 / 00094 Instituto de Salud Carlos III | Ref. PI17 / 00837

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/publishedVersion