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1.
Sensors (Basel) ; 21(24)2021 Dec 09.
Artigo em Inglês | MEDLINE | ID: mdl-34960336

RESUMO

Voxel-based data structures, algorithms, frameworks, and interfaces have been used in computer graphics and many other applications for decades. There is a general necessity to seek adequate digital representations, such as voxels, that would secure unified data structures, multi-resolution options, robust validation procedures and flexible algorithms for different 3D tasks. In this review, we evaluate the most common properties and algorithms for voxelisation of 2D and 3D objects. Thus, many voxelisation algorithms and their characteristics are presented targeting points, lines, triangles, surfaces and solids as geometric primitives. For lines, we identify three groups of algorithms, where the first two achieve different voxelisation connectivity, while the third one presents voxelisation of curves. We can say that surface voxelisation is a more desired voxelisation type compared to solid voxelisation, as it can be achieved faster and requires less memory if voxels are stored in a sparse way. At the same time, we evaluate in the paper the available voxel data structures. We split all data structures into static and dynamic grids considering the frequency to update a data structure. Static grids are dominated by SVO-based data structures focusing on memory footprint reduction and attributes preservation, where SVDAG and SSVDAG are the most advanced methods. The state-of-the-art dynamic voxel data structure is NanoVDB which is superior to the rest in terms of speed as well as support for out-of-core processing and data management, which is the key to handling large dynamically changing scenes. Overall, we can say that this is the first review evaluating the available voxelisation algorithms for different geometric primitives as well as voxel data structures.


Assuntos
Algoritmos , Gráficos por Computador , Imageamento Tridimensional
2.
Mol Syst Biol ; 17(10): e10387, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34664389

RESUMO

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.


Assuntos
COVID-19/imunologia , Biologia Computacional/métodos , Bases de Dados Factuais , SARS-CoV-2/imunologia , Software , Antivirais/uso terapêutico , COVID-19/tratamento farmacológico , COVID-19/genética , COVID-19/virologia , Gráficos por Computador , Citocinas/genética , Citocinas/imunologia , Mineração de Dados/estatística & dados numéricos , Regulação da Expressão Gênica , Interações entre Hospedeiro e Microrganismos/genética , Interações entre Hospedeiro e Microrganismos/imunologia , Humanos , Imunidade Celular/efeitos dos fármacos , Imunidade Humoral/efeitos dos fármacos , Imunidade Inata/efeitos dos fármacos , Linfócitos/efeitos dos fármacos , Linfócitos/imunologia , Linfócitos/virologia , Redes e Vias Metabólicas/genética , Redes e Vias Metabólicas/imunologia , Células Mieloides/efeitos dos fármacos , Células Mieloides/imunologia , Células Mieloides/virologia , Mapeamento de Interação de Proteínas , SARS-CoV-2/efeitos dos fármacos , SARS-CoV-2/genética , SARS-CoV-2/patogenicidade , Transdução de Sinais , Fatores de Transcrição/genética , Fatores de Transcrição/imunologia , Proteínas Virais/genética , Proteínas Virais/imunologia
3.
OMICS ; 25(11): 681-692, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34678084

RESUMO

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.


Assuntos
Biologia Computacional/tendências , Genômica/tendências , Metabolômica/tendências , Proteômica/tendências , Animais , COVID-19 , Gráficos por Computador , Epigenômica/tendências , Perfilação da Expressão Gênica/tendências , Humanos , Farmacogenética/tendências , Publicações , SARS-CoV-2 , Terminologia como Assunto
4.
PLoS Comput Biol ; 17(9): e1009444, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34570769

RESUMO

Transcription factors (TFs) are proteins that promote or reduce the expression of genes by binding short genomic DNA sequences known as transcription factor binding sites (TFBS). While several tools have been developed to scan for potential occurrences of TFBS in linear DNA sequences or reference genomes, no tool exists to find them in pangenome variation graphs (VGs). VGs are sequence-labelled graphs that can efficiently encode collections of genomes and their variants in a single, compact data structure. Because VGs can losslessly compress large pangenomes, TFBS scanning in VGs can efficiently capture how genomic variation affects the potential binding landscape of TFs in a population of individuals. Here we present GRAFIMO (GRAph-based Finding of Individual Motif Occurrences), a command-line tool for the scanning of known TF DNA motifs represented as Position Weight Matrices (PWMs) in VGs. GRAFIMO extends the standard PWM scanning procedure by considering variations and alternative haplotypes encoded in a VG. Using GRAFIMO on a VG based on individuals from the 1000 Genomes project we recover several potential binding sites that are enhanced, weakened or missed when scanning only the reference genome, and which could constitute individual-specific binding events. GRAFIMO is available as an open-source tool, under the MIT license, at https://github.com/pinellolab/GRAFIMO and https://github.com/InfOmics/GRAFIMO.


Assuntos
Variação Genética , Motivos de Nucleotídeos , Software , Fatores de Transcrição/metabolismo , Sequência de Bases , Sítios de Ligação/genética , Biologia Computacional , Gráficos por Computador , Genoma Humano , Genômica , Haplótipos , Humanos , Ligação Proteica/genética
5.
PLoS Comput Biol ; 17(9): e1009037, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34570773

RESUMO

Graph representations are traditionally used to represent protein structures in sequence design protocols in which the protein backbone conformation is known. This infrequently extends to machine learning projects: existing graph convolution algorithms have shortcomings when representing protein environments. One reason for this is the lack of emphasis on edge attributes during massage-passing operations. Another reason is the traditionally shallow nature of graph neural network architectures. Here we introduce an improved message-passing operation that is better equipped to model local kinematics problems such as protein design. Our approach, XENet, pays special attention to both incoming and outgoing edge attributes. We compare XENet against existing graph convolutions in an attempt to decrease rotamer sample counts in Rosetta's rotamer substitution protocol, used for protein side-chain optimization and sequence design. This use case is motivating because it both reduces the size of the search space for classical side-chain optimization algorithms, and allows larger protein design problems to be solved with quantum algorithms on near-term quantum computers with limited qubit counts. XENet outperformed competing models while also displaying a greater tolerance for deeper architectures. We found that XENet was able to decrease rotamer counts by 40% without loss in quality. This decreased the memory consumption for classical pre-computation of rotamer energies in our use case by more than a factor of 3, the qubit consumption for an existing sequence design quantum algorithm by 40%, and the size of the solution space by a factor of 165. Additionally, XENet displayed an ability to handle deeper architectures than competing convolutions.


Assuntos
Algoritmos , Gráficos por Computador , Desenho Assistido por Computador , Aprendizado de Máquina , Proteínas/química , Biologia Computacional , Computadores , Modelos Moleculares , Redes Neurais de Computação , Conformação Proteica
6.
PLoS One ; 16(9): e0249257, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34492015

RESUMO

Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation-including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.


Assuntos
Algoritmos , Rastreamento de Células/métodos , Gráficos por Computador , Bases de Dados Factuais , Humanos
7.
J Chem Inf Model ; 61(10): 5293-5303, 2021 10 25.
Artigo em Inglês | MEDLINE | ID: mdl-34528431

RESUMO

Building and displaying all-atom models of biomolecular structures with millions or billions of atoms, like virus particles or cells, remain a challenge due to the sheer size of the data, the required levels of automated building, and the visualization limits of today's graphics hardware. Based on concepts introduced with the CellPack program, we report new algorithms to create such large-scale models using an intermediate coarse-grained "pet representation" of biomolecules with 1/10th the normal size. Pet atoms are placed such that they optimally trace the surface of the original molecule with just ∼1/50th the original atom number and are joined with covalent bonds. Molecular dynamics simulations of pet molecules allow for efficient packing optimization, as well as the generation of realistic DNA/RNA conformations. This pet world can be expanded back to the all-atom representation to be explored and visualized with full details. Essential for the efficient interactive visualization of gigastructures is the use of multiple levels of detail (LODs), where distant molecules are drawn with a heavily reduced polygon count. We present a grid-based algorithm to create such LODs for all common molecular graphics styles (including ball-and-sticks, ribbons, and cartoons) that do not require monochrome molecules to hide LOD transitions. As a practical application, we built all-atom models of SARS-CoV-2, HIV, and an entire presynaptic bouton with 1 µm diameter and 3.6 billion atoms, using modular building blocks to significantly reduce GPU memory requirements through instancing. We employ the Vulkan graphics API to maximize performance on consumer grade hardware and describe how to use the mmCIF format to efficiently store such giant models. An implementation is available as part of the YASARA molecular modeling and simulation program from www.YASARA.org. The free YASARA View program can be used to explore the presented models, which can be downloaded from www.YASARA.org/petworld, a Creative Commons platform for sharing giant biomolecular structures.


Assuntos
COVID-19 , Gráficos por Computador , Algoritmos , Humanos , Simulação de Dinâmica Molecular , SARS-CoV-2
9.
PLoS Comput Biol ; 17(8): e1009227, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34351901

RESUMO

For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/ß-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.


Assuntos
Modelos Biológicos , Via de Sinalização Wnt , Animais , Fenômenos Bioquímicos , Biologia Computacional , Gráficos por Computador , Simulação por Computador , Humanos , Software , Biologia de Sistemas
10.
Elife ; 102021 08 24.
Artigo em Inglês | MEDLINE | ID: mdl-34427185

RESUMO

Insect neuroscience generates vast amounts of highly diverse data, of which only a small fraction are findable, accessible and reusable. To promote an open data culture, we have therefore developed the InsectBrainDatabase (IBdb), a free online platform for insect neuroanatomical and functional data. The IBdb facilitates biological insight by enabling effective cross-species comparisons, by linking neural structure with function, and by serving as general information hub for insect neuroscience. The IBdb allows users to not only effectively locate and visualize data, but to make them widely available for easy, automated reuse via an application programming interface. A unique private mode of the database expands the IBdb functionality beyond public data deposition, additionally providing the means for managing, visualizing, and sharing of unpublished data. This dual function creates an incentive for data contribution early in data management workflows and eliminates the additional effort normally associated with publicly depositing research data.


Assuntos
Pesquisa Biomédica , Encéfalo/fisiologia , Bases de Dados Factuais , Gestão da Informação , Armazenamento e Recuperação da Informação , Insetos/fisiologia , Fenômenos Fisiológicos do Sistema Nervoso , Neurociências , Animais , Encéfalo/anatomia & histologia , Gráficos por Computador , Mineração de Dados , Insetos/anatomia & histologia , Internet , Interface Usuário-Computador
12.
Int J Mol Sci ; 22(14)2021 Jul 13.
Artigo em Inglês | MEDLINE | ID: mdl-34299110

RESUMO

Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on High Performance Computing. AMIDE version 2 brings substantial speed-up improvement by using AutoDock-GPU and by pulling a total revision of programming workflow, leading to better performances, easier use, bug corrections, parallelization improvements and PC/HPC compatibility. In addition to inverse docking, AMIDE is now an optimized tool capable of high throughput inverse screening. For instance, AMIDE version 2 allows acceleration of the docking up to 12.4 times for 100 runs of AutoDock compared to version 1, without significant changes in docking poses. The reverse docking of a ligand on 87 proteins takes only 23 min on 1 GPU (Graphics Processing Unit), while version 1 required 300 cores to reach the same execution time. Moreover, we have shown an exponential acceleration of the computation time as a function of the number of GPUs used, allowing a significant reduction of the duration of the inverse docking process on large datasets.


Assuntos
Algoritmos , Ensaios de Triagem em Larga Escala/métodos , Simulação de Acoplamento Molecular , Preparações Farmacêuticas/química , Proteínas/química , Software , Gráficos por Computador , Humanos , Ligantes , Reprodutibilidade dos Testes , Fluxo de Trabalho
13.
PLoS Comput Biol ; 17(7): e1009229, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34280186

RESUMO

Graphs such as de Bruijn graphs and OLC (overlap-layout-consensus) graphs have been widely adopted for the de novo assembly of genomic short reads. This work studies another important problem in the field: how graphs can be used for high-performance compression of the large-scale sequencing data. We present a novel graph definition named Hamming-Shifting graph to address this problem. The definition originates from the technological characteristics of next-generation sequencing machines, aiming to link all pairs of distinct reads that have a small Hamming distance or a small shifting offset or both. We compute multiple lexicographically minimal k-mers to index the reads for an efficient search of the weight-lightest edges, and we prove a very high probability of successfully detecting these edges. The resulted graph creates a full mutual reference of the reads to cascade a code-minimized transfer of every child-read for an optimal compression. We conducted compression experiments on the minimum spanning forest of this extremely sparse graph, and achieved a 10 - 30% more file size reduction compared to the best compression results using existing algorithms. As future work, the separation and connectivity degrees of these giant graphs can be used as economical measurements or protocols for quick quality assessment of wet-lab machines, for sufficiency control of genomic library preparation, and for accurate de novo genome assembly.


Assuntos
Algoritmos , Compressão de Dados/métodos , Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Animais , Biologia Computacional , Gráficos por Computador , Compressão de Dados/estatística & dados numéricos , Bases de Dados Genéticas/estatística & dados numéricos , Genômica/estatística & dados numéricos , Sequenciamento de Nucleotídeos em Larga Escala/estatística & dados numéricos , Humanos
15.
Genes (Basel) ; 12(7)2021 06 29.
Artigo em Inglês | MEDLINE | ID: mdl-34209818

RESUMO

This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG's usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.


Assuntos
COVID-19 , Bases de Conhecimento , COVID-19/epidemiologia , COVID-19/etiologia , Cloroquina/farmacologia , Gráficos por Computador , Bases de Dados Factuais , Doença pelo Vírus Ebola/tratamento farmacológico , Humanos , Hidroxicloroquina/farmacologia , Reconhecimento Automatizado de Padrão , Peptidil Dipeptidase A/genética , PubMed , Receptores de Interleucina-6/sangue , SARS-CoV-2 , Fator de Transcrição STAT1
17.
J Phys Chem A ; 125(25): 5633-5642, 2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-34142824

RESUMO

Computational approaches for predicting drug-target interactions (DTIs) play an important role in drug discovery since conventional screening experiments are time-consuming and expensive. In this study, we proposed end-to-end representation learning of a graph neural network with an attention mechanism and an attentive bidirectional long short-term memory (BiLSTM) to predict DTIs. For efficient training, we introduced a bidirectional encoder representations from transformers (BERT) pretrained method to extract substructure features from protein sequences and a local breadth-first search (BFS) to learn subgraph information from molecular graphs. Integrating both models, we developed a DTI prediction system. As a result, the proposed method achieved high performances with increases of 2.4% and 9.4% for AUC and recall, respectively, on unbalanced datasets compared with other methods. Extensive experiments showed that our model can relatively screen potential drugs for specific protein. Furthermore, visualizing the attention weights provides biological insight.


Assuntos
Biologia Computacional/métodos , Gráficos por Computador , Aprendizado Profundo , Descoberta de Drogas/métodos , Preparações Farmacêuticas/metabolismo , Proteínas/química , Proteínas/metabolismo , Sequência de Aminoácidos
18.
Acta Crystallogr D Struct Biol ; 77(Pt 6): 712-726, 2021 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-34076587

RESUMO

In this contribution, the current protocols for modelling covalent linkages within the CCP4 suite are considered. The mechanism used for modelling covalent linkages is reviewed: the use of dictionaries for describing changes to stereochemistry as a result of the covalent linkage and the application of link-annotation records to structural models to ensure the correct treatment of individual instances of covalent linkages. Previously, linkage descriptions were lacking in quality compared with those of contemporary component dictionaries. Consequently, AceDRG has been adapted for the generation of link dictionaries of the same quality as for individual components. The approach adopted by AceDRG for the generation of link dictionaries is outlined, which includes associated modifications to the linked components. A number of tools to facilitate the practical modelling of covalent linkages available within the CCP4 suite are described, including a new restraint-dictionary accumulator, the Make Covalent Link tool and AceDRG interface in Coot, the 3D graphical editor JLigand and the mechanisms for dealing with covalent linkages in the CCP4i2 and CCP4 Cloud environments. These integrated solutions streamline and ease the covalent-linkage modelling workflow, seamlessly transferring relevant information between programs. Current recommended practice is elucidated by means of instructive practical examples. By summarizing the different approaches to modelling linkages that are available within the CCP4 suite, limitations and potential pitfalls that may be encountered are highlighted in order to raise awareness, with the intention of improving the quality of future modelled covalent linkages in macromolecular complexes.


Assuntos
Substâncias Macromoleculares/química , Modelos Moleculares , Proteínas/química , Software , Gráficos por Computador , Cristalografia por Raios X , Interface Usuário-Computador
19.
Nucleic Acids Res ; 49(W1): W153-W161, 2021 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-34125897

RESUMO

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.


Assuntos
Doença/genética , Software , Esclerose Amiotrófica Lateral/genética , Gráficos por Computador , Genes , Humanos , Aprendizado de Máquina
20.
Eur J Epidemiol ; 36(7): 659-667, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34114186

RESUMO

Causal graphs provide a key tool for optimizing the validity of causal effect estimates. Although a large literature exists on the mathematical theory underlying the use of causal graphs, less literature exists to aid applied researchers in understanding how best to develop and use causal graphs in their research projects. We sought to understand why researchers do or do not regularly use DAGs by surveying practicing epidemiologists and medical researchers on their knowledge, level of interest, attitudes, and practices towards the use of causal graphs in applied epidemiology and health research. We used Twitter and the Society for Epidemiologic Research to disseminate the survey. Overall, a majority of participants reported being comfortable with using causal graphs and reported using them 'sometimes', 'often', or 'always' in their research. Having received training appeared to improve comprehension of the assumptions displayed in causal graphs. Many of the respondents who did not use causal graphs reported lack of knowledge as a barrier to using DAGs in their research. Causal graphs are of interest to epidemiologists and medical researchers, but there are several barriers to their uptake. Additional training and clearer guidance are needed. In addition, methodological developments regarding visualization of effect measure modification and interaction on causal graphs is needed.


Assuntos
Atitude do Pessoal de Saúde , Causalidade , Gráficos por Computador , Interpretação Estatística de Dados , Projetos de Pesquisa Epidemiológica , Epidemiologistas , Feminino , Humanos , Masculino , Pesquisa Qualitativa , Pesquisadores , Inquéritos e Questionários
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