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1.
Database (Oxford) ; 20222022 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-35363306

RESUMO

The Sickle Cell Disease (SCD) Ontology (SCDO, https://scdontology.h3abionet.org/) provides a comprehensive knowledge base of SCD management, systems and standardized human and machine-readable resources that unambiguously describe terminology and concepts about SCD for researchers, patients and clinicians. The SCDO was launched in 2016 and is continuously updated in quantity, as well as in quality, to effectively support the curation of SCD research, patient databasing and clinical informatics applications. SCD knowledge from the scientific literature is used to update existing SCDO terms and create new terms where necessary. Here, we report major updates to the SCDO, from December 2019 until April 2021, for promoting interoperability and facilitating SCD data harmonization, sharing and integration across different studies and for retrospective multi-site research collaborations. SCDO developers continue to collaborate with the SCD community, clinicians and researchers to improve specific ontology areas and expand standardized descriptions to conditions influencing SCD phenotypic expressions and clinical manifestations of the sickling process, e.g. thalassemias. Database URL: https://scdontology.h3abionet.org/.


Assuntos
Anemia Falciforme , Anemia Falciforme/genética , Bases de Dados Factuais , Humanos , Bases de Conhecimento , Fenótipo , Estudos Retrospectivos
2.
Front Immunol ; 13: 863110, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35401578

RESUMO

RAG1 and RAG2 genes generate diversity in immunoglobulin and TCR genes by initiating the process of V-D-J recombination. RAGs recognize specific sequences (heptamer-nonamer) to generate a diversity of immunoglobulins. RAG expression is limited to early B and T cell developmental stages. Aberrant expression of RAG can lead to double strand breaks and translocations as observed in leukemia and lymphoma. The expression of RAG is tightly regulated at the transcriptional and posttranscriptional levels. MicroRNAs (miRNAs) are small non-coding RNAs that are involved in the post-transcriptional regulation of gene expression. This study aimed to identify and catalog RAG regulation by miRNA during normal development and cancer. NGS data from normal B-cell and T-cell developmental stages and blood cancer samples have been analyzed for the expression of miRNAs against RAG1 (1,173 against human RAG1 and 749 against mouse RAG1). The analyzed data has been organized to retrieve the miRNA and mRNA expression of various RAG regulators (10 transcription factors and interacting partners) in normal and diseased states. The database allows users to navigate through the human and mouse RAG regulators, visualize and plot expression. miRAGDB is freely available and can be accessed at http://52.4.112.252/shiny/miragdb/.


Assuntos
Linfoma , MicroRNAs , Animais , Proteínas de Homeodomínio/genética , Proteínas de Homeodomínio/metabolismo , Bases de Conhecimento , Linfoma/genética , Camundongos , MicroRNAs/genética , Recombinação V(D)J
3.
Comput Intell Neurosci ; 2022: 4293102, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35387240

RESUMO

How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction and cluster center point selection are key issues in text clustering research. Common keyword extraction algorithms can be divided into three categories: semantic-based algorithms, machine learning-based algorithms, and statistical model-based algorithms. There are three common methods for selecting cluster centers: randomly selecting the initial cluster center point, manually specifying the cluster center point, and selecting the cluster center point according to the similarity between the points to be clustered. The randomly selected initial cluster center points may contain "outliers," and the clustering results are locally optimal. Manually specifying the cluster center points will be very subjective because each person's understanding of the text set is different, and it is not suitable for the case of a large number of text sets. Selecting the cluster center points according to the similarity between the points to be clustered can make the selected cluster center points distributed in each class and be as close as possible to the class center points, but it takes a long time to calculate the cluster centers. Aiming at this problem, this paper proposes a keyword extraction algorithm based on cluster analysis. The results show that the algorithm does not rely on background knowledge bases, dictionaries, etc., and obtains statistical parameters and builds models through training. Experiments show that the keyword extraction algorithm has high accuracy and can quickly extract the subject content of an English translation.


Assuntos
Algoritmos , Aprendizado de Máquina , Análise por Conglomerados , Humanos , Bases de Conhecimento , Modelos Estatísticos
4.
Neural Netw ; 151: 222-237, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-35439666

RESUMO

Most existing automatic kinship verification methods focus on learning the optimal distance metrics between family members. However, learning facial features and kinship features simultaneously may cause the proposed models to be too weak. In this work, we explore the possibility of bridging this gap by developing knowledge-based tensor models based on pre-trained multi-view models. We propose an effective knowledge-based tensor similarity extraction framework for automatic facial kinship verification using four pre-trained networks (i.e., VGG-Face, VGG-F, VGG-M, and VGG-S). Therefore, knowledge-based deep face and general features (such as identity, age, gender, ethnicity, expression, lighting, pose, contour, edges, corners, shape, etc.) were successfully fused by our tensor design to understand the kinship cue. Multiple effective representations are learned for kinship verification statements (children and parents) using a margin maximization learning scheme based on Tensor Cross-view Quadratic Exponential Discriminant Analysis. Through the exponential learning process, the large gap between distributions of the same family can be reduced to the maximum, while the small gap between distributions of different families is simultaneously increased. The WCCN metric successfully reduces the intra-class variability problem caused by deep features. The explanation of black-box models and the problems of ubiquitous face recognition are considered in our system. The extensive experiments on four challenging datasets show that our system performs very well compared to state-of-the-art approaches.


Assuntos
Algoritmos , Reconhecimento Automatizado de Padrão , Criança , Face , Família , Humanos , Bases de Conhecimento , Reconhecimento Automatizado de Padrão/métodos
5.
BMC Med Inform Decis Mak ; 22(1): 56, 2022 03 02.
Artigo em Inglês | MEDLINE | ID: mdl-35236355

RESUMO

BACKGROUND: Personalized medicine tailors care based on the patient's or pathogen's genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians. METHODS: We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting. RESULTS: We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%. CONCLUSION: Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Tuberculose Resistente a Múltiplos Medicamentos , Humanos , Bases de Conhecimento , Aprendizado de Máquina , Medicina de Precisão , Tuberculose Resistente a Múltiplos Medicamentos/tratamento farmacológico
6.
Methods Mol Biol ; 2405: 403-424, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35298824

RESUMO

The design of proteins and miniproteins is an important challenge. Designed variants should be stable, meaning the folded/unfolded free energy difference should be large enough. Thus, the unfolded state plays a central role. An extended peptide model is often used, where side chains interact with solvent and nearby backbone, but not each other. The unfolded energy is then a function of sequence composition only and can be empirically parametrized. If the space of sequences is explored with a Monte Carlo procedure, protein variants will be sampled according to a well-defined Boltzmann probability distribution. We can then choose unfolded model parameters to maximize the probability of sampling native-like sequences. This leads to a well-defined maximum likelihood framework. We present an iterative algorithm that follows the likelihood gradient. The method is presented in the context of our Proteus software, as a detailed downloadable tutorial. The unfolded model is combined with a folded model that uses molecular mechanics and a Generalized Born solvent. It was optimized for three PDZ domains and then used to redesign them. The sequences sampled are native-like and similar to a recent PDZ design study that was experimentally validated.


Assuntos
Dobramento de Proteína , Proteínas , Bases de Conhecimento , Simulação de Dinâmica Molecular , Peptídeos/química , Peptídeos/genética , Proteínas/química
7.
Database (Oxford) ; 20222022 03 28.
Artigo em Inglês | MEDLINE | ID: mdl-35348650

RESUMO

ABSTRACT: Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based on Reactome pathways against published empirical observations. Ten cancer-relevant Reactome pathways, representing diverse biological processes such as signal transduction, cell division, DNA repair and transcriptional regulation, were selected for testing. For each pathway, root input nodes and key pathway outputs were defined. We then used pathway-diagram-derived logic graphs to predict, either by inspection by biocurators or using a novel algorithm MP-BioPath, the effects of bidirectional perturbations (upregulation/activation or downregulation/inhibition) of single root inputs on the status of key outputs. These predictions were then compared to published empirical tests. In total, 4968 test cases were analyzed across 10 pathways, of which 847 were supported by published empirical findings. Out of the 847 test cases, curators' predictions agreed with the experimental evidence in 670 and disagreed in 177 cases, resulting in ∼81% overall accuracy. MP-BioPath predictions agreed with experimental evidence for 625 and disagreed for 222 test cases, resulting in ∼75% overall accuracy. The expected accuracy of random guessing was 33%. Per-pathway accuracy did not correlate with the number of pathway edges nor the number of pathway nodes but varied across pathways, ranging from 56% (curator)/44% (MP-BioPath) for 'Mitotic G1 phase and G1/S transition' to 100% (curator)/94% (MP-BioPath) for 'RAF/MAP kinase cascade'. This study highlights the potential of pathway databases such as Reactome in modeling genetic perturbations, promoting standardization of experimental pathway activity readout and supporting hypothesis-driven research by revealing relationships between pathway inputs and outputs that have not yet been directly experimentally tested. DATABASE URL: www.reactome.org.


Assuntos
Fenômenos Biológicos , Bases de Conhecimento , Algoritmos , Bases de Dados Factuais , Humanos , Transdução de Sinais
8.
Comput Intell Neurosci ; 2022: 5649994, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35126495

RESUMO

When TextRank algorithm based on graph model constructs graph associative edges, the co-occurrence window rules only consider the relationships between local terms. Using the information in the document itself is limited. In order to solve the above problems, an improved TextRank keyword extraction algorithm based on rough data reasoning combined with word vector clustering, RDD-WRank, was proposed. Firstly, the algorithm uses rough data reasoning to mine the association between candidate keywords, expands the search scope, and makes the results more comprehensive. Then, based on Wikipedia online open knowledge base, word embedding technology is used to integrate Word2Vec into the improved algorithm, and the word vector of TextRank lexical graph nodes is clustered to adjust the voting importance of nodes in the cluster. Compared with the traditional TextRank algorithm and the Word2Vec algorithm combined with TextRank, the experimental results show that the improved algorithm has significantly improved the extraction accuracy, which proves that the idea of using rough data reasoning can effectively improve the performance of the algorithm to extract keywords.


Assuntos
Algoritmos , Bases de Conhecimento , Análise por Conglomerados , Resolução de Problemas
9.
Sensors (Basel) ; 22(3)2022 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-35161754

RESUMO

Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.


Assuntos
Poluentes Atmosféricos , Poluição do Ar em Ambientes Fechados , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Poluição do Ar em Ambientes Fechados/análise , Monitoramento Ambiental , Bases de Conhecimento , Material Particulado/análise
10.
Stud Health Technol Inform ; 288: 100-112, 2022 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-35102832

RESUMO

Donald A.B. Lindberg M.D. arrived at the U.S. National Library of Medicine in 1984 and quickly launched the Unified Medical Language System (UMLS) research and development project to help computer understand biomedical meaning and to enable retrieval and integration of information from disparate electronic sources, e.g., patient records, biomedical literature, knowledge bases. This chapter focuses on how Lindberg's thinking, preferred ways of working, and decision-making guided UMLS goals and development and on what made the UMLS markedly "new and different" and ahead of its time.


Assuntos
Bases de Conhecimento , Unified Medical Language System , Humanos , National Library of Medicine (U.S.) , Estados Unidos
11.
Sensors (Basel) ; 22(4)2022 Feb 17.
Artigo em Inglês | MEDLINE | ID: mdl-35214484

RESUMO

Collaborative reasoning for knowledge-based visual question answering is challenging but vital and efficient in understanding the features of the images and questions. While previous methods jointly fuse all kinds of features by attention mechanism or use handcrafted rules to generate a layout for performing compositional reasoning, which lacks the process of visual reasoning and introduces a large number of parameters for predicting the correct answer. For conducting visual reasoning on all kinds of image-question pairs, in this paper, we propose a novel reasoning model of a question-guided tree structure with a knowledge base (QGTSKB) for addressing these problems. In addition, our model consists of four neural module networks: the attention model that locates attended regions based on the image features and question embeddings by attention mechanism, the gated reasoning model that forgets and updates the fused features, the fusion reasoning model that mines high-level semantics of the attended visual features and knowledge base and knowledge-based fact model that makes up for the lack of visual and textual information with external knowledge. Therefore, our model performs visual analysis and reasoning based on tree structures, knowledge base and four neural module networks. Experimental results show that our model achieves superior performance over existing methods on the VQA v2.0 and CLVER dataset, and visual reasoning experiments prove the interpretability of the model.


Assuntos
Bases de Conhecimento , Redes Neurais de Computação , Aprendizagem , Resolução de Problemas , Semântica
12.
Nat Commun ; 13(1): 756, 2022 02 09.
Artigo em Inglês | MEDLINE | ID: mdl-35140225

RESUMO

Manual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we introduce a Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework that facilitates the interpretation and reporting of somatic variants in cancer. PORI integrates reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents an open-source platform alternative to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 pan-cancer genome atlas tumours to the graph knowledge base, calculating therapeutically informative alterations, and making available reports describing select individual samples.


Assuntos
Carcinogênese/genética , Neoplasias/genética , Biomarcadores Tumorais , Bases de Dados Genéticas , Variação Genética , Genômica , Humanos , Bases de Conhecimento , Medicina de Precisão
13.
Database (Oxford) ; 2022(2022)2022 01 29.
Artigo em Inglês | MEDLINE | ID: mdl-35134132

RESUMO

The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction.


Assuntos
Mineração de Dados , Bases de Conhecimento , Resistência Microbiana a Medicamentos/genética
14.
J Biomed Semantics ; 13(1): 4, 2022 01 31.
Artigo em Inglês | MEDLINE | ID: mdl-35101121

RESUMO

BACKGROUND: Electronic Laboratory Notebooks (ELNs) are used to document experiments and investigations in the wet-lab. Protocols in ELNs contain a detailed description of the conducted steps including the necessary information to understand the procedure and the raised research data as well as to reproduce the research investigation. The purpose of this study is to investigate whether such ELN protocols can be used to create semantic documentation of the provenance of research data by the use of ontologies and linked data methodologies. METHODS: Based on an ELN protocol of a biomedical wet-lab experiment, a retrospective provenance model of the raised research data describing the details of the experiment in a machine-interpretable way is manually engineered. Furthermore, an automated approach for knowledge acquisition from ELN protocols is derived from these results. This structure-based approach exploits the structure in the experiment's description such as headings, tables, and links, to translate the ELN protocol into a semantic knowledge representation. To satisfy the Findable, Accessible, Interoperable, and Reuseable (FAIR) guiding principles, a ready-to-publish bundle is created that contains the research data together with their semantic documentation. RESULTS: While the manual modelling efforts serve as proof of concept by employing one protocol, the automated structure-based approach demonstrates the potential generalisation with seven ELN protocols. For each of those protocols, a ready-to-publish bundle is created and, by employing the SPARQL query language, it is illustrated that questions about the processes and the obtained research data can be answered. CONCLUSIONS: The semantic documentation of research data obtained from the ELN protocols allows for the representation of the retrospective provenance of research data in a machine-interpretable way. Research Object Crate (RO-Crate) bundles including these models enable researchers to easily share the research data including the corresponding documentation, but also to search and relate the experiment to each other.


Assuntos
Documentação , Bases de Conhecimento , Documentação/métodos , Eletrônica , Estudos Retrospectivos , Web Semântica
15.
J Am Med Inform Assoc ; 29(4): 585-591, 2022 03 15.
Artigo em Inglês | MEDLINE | ID: mdl-35190824

RESUMO

Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including "safe failure"; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence-aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications.


Assuntos
Inteligência Artificial , Medicina , Atenção à Saúde , Instalações de Saúde , Bases de Conhecimento
16.
Med Phys ; 49(4): 2193-2202, 2022 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-35157318

RESUMO

BACKGROUND: Knowledge-based planning (KBP) is increasingly implemented clinically because of its demonstrated ability to improve treatment planning efficiency and reduce plan quality variations. However, cases with large dose-volume histogram (DVH) prediction uncertainties may still need manual adjustments by the planner to achieve high plan quality. PURPOSE: The purpose of this study is to develop a data-driven method to detect patients with high prediction uncertainties so that intentional effort is directed to these patients. METHODS: We apply an anomaly detection method known as the local outlier factor (LOF) to a dataset consisting of the training set and each of the prospective patients considered, to evaluate their likelihood of being an anomaly when compared with the training cases. Features used in the LOF analysis include anatomical features and the model-generated DVH principal component scores. To test the efficacy of the proposed model, 142 prostate patients were retrieved from the clinical database and split into a training dataset of 100 patients and a test dataset of 42 patients. The outlier identification performance was quantified by the difference between the DVH prediction root-mean-squared errors (RMSE) of the identified outlier cases and that of the remaining inlier cases. RESULTS: With a predefined LOF threshold of 1.4, the inlier cases achieved average RMSEs of 5.0 and 6.7 for bladder and rectum, while the outlier cases have substantially higher RMSEs of 6.7 and 13.0 in comparison. CONCLUSIONS: We propose a method that can determine the prospective patient's outlier status. This method can be integrated into existing automated treatment planning workflows to reduce the risk of generating suboptimal treatment plans while providing an upfront alert to the treatment planner.


Assuntos
Planejamento da Radioterapia Assistida por Computador , Radioterapia de Intensidade Modulada , Humanos , Bases de Conhecimento , Masculino , Órgãos em Risco , Pelve , Estudos Prospectivos , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador/métodos , Radioterapia de Intensidade Modulada/métodos
17.
Sensors (Basel) ; 22(4)2022 Feb 10.
Artigo em Inglês | MEDLINE | ID: mdl-35214240

RESUMO

As an important field of computer vision, object detection has been studied extensively in recent years. However, existing object detection methods merely utilize the visual information of the image and fail to mine the high-level semantic information of the object, which leads to great limitations. To take full advantage of multi-source information, a knowledge update-based multimodal object recognition model is proposed in this paper. Specifically, our method initially uses Faster R-CNN to regionalize the image, then applies a transformer-based multimodal encoder to encode visual region features (region-based image features) and textual features (semantic relationships between words) corresponding to pictures. After that, a graph convolutional network (GCN) inference module is introduced to establish a relational network in which the points denote visual and textual region features, and the edges represent their relationships. In addition, based on an external knowledge base, our method further enhances the region-based relationship expression capability through a knowledge update module. In summary, the proposed algorithm not only learns the accurate relationship between objects in different regions of the image, but also benefits from the knowledge update through an external relational database. Experimental results verify the effectiveness of the proposed knowledge update module and the independent reasoning ability of our model.


Assuntos
Redes Neurais de Computação , Semântica , Algoritmos , Conhecimento , Bases de Conhecimento
18.
J Biomed Semantics ; 13(1): 1, 2022 01 06.
Artigo em Inglês | MEDLINE | ID: mdl-34991705

RESUMO

BACKGROUND: The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment's computational and non-computational processes. RESULTS: We present the "REPRODUCE-ME" data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps. CONCLUSION: We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy.


Assuntos
Bases de Conhecimento , Semântica , Reprodutibilidade dos Testes , Web Semântica
19.
Stud Health Technol Inform ; 289: 45-48, 2022 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-35062088

RESUMO

Considering the growing interest towards next generation sequencing (NGS) and data analysis, and the substantial challenges associated to fully exploiting these technologies and data without the proper experience, an expert knowledge-based user-friendly analytical tool was developed to allow non-bioinformatics experts to process NGS genomic data, automatically prioritise genomic variants and make their own annotations. This tool was developed using a user-centred methodology, where an iterative process was followed until a useful product was developed. This tool allows the users to set-up the pre-processing pipeline, filter the obtained data, annotate it using external and local databases (DBs) and help on deciding which variants are more relevant for each study, taking advantage of its customised expert-based scoring system. The end users involved in the project concluded that CRIBOMICS was easy to learn, use and interact with, reducing the analysis time and possible errors of variant prioritisation for genetic diagnosis.


Assuntos
Variação Genética , Software , Biologia Computacional , Variação Genética/genética , Genômica , Sequenciamento de Nucleotídeos em Larga Escala , Bases de Conhecimento
20.
BMC Med Inform Decis Mak ; 22(1): 23, 2022 01 28.
Artigo em Inglês | MEDLINE | ID: mdl-35090449

RESUMO

INTRODUCTION: Currently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness. METHODS: This study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm. RESULTS: We developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites. DISCUSSION AND CONCLUSION: Our findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms.


Assuntos
Algoritmos , Registros Eletrônicos de Saúde , Genômica , Humanos , Bases de Conhecimento , Fenótipo
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