Towards Using Graph Analytics for Tracking Covid-19

Mouden, Zakariyaa Ait El; Taj, R. Moulay; Jakimi, Abdeslam; Hajar, Moha

doi:10.1016/j.procs.2020.10.029

Cited by 11 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A knowledge base built upon the integration of biomedical entities from such a literature hub would provide tremendous value in the exploration of explicit or implicit associations among diverse biomedical entities as investigators attempt to answer clinical questions related to COVID-19. A number of recently published journal articles have included graph-based analysis of COVID-19 data sets [ 3 ]. For example, Groza [ 4 ] analyzed how a semantically annotated data set would be helpful in detecting and preventing potentially harmful misinformation regarding the spread of COVID-19 based on CORD-19-on-FHIR (a linked data version of the COVID-19 Open Research Dataset [CORD-19] data represented in FHIR RDF by mining the CORD-19 data set and adding semantic annotations) [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation

Jiang¹,

Ngo²,

Chapman³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

Background Multiple types of biomedical associations of knowledge graphs, including COVID-19–related ones, are constructed based on co-occurring biomedical entities retrieved from recent literature. However, the applications derived from these raw graphs (eg, association predictions among genes, drugs, and diseases) have a high probability of false-positive predictions as co-occurrences in the literature do not always mean there is a true biomedical association between two entities. Objective Data quality plays an important role in training deep neural network models; however, most of the current work in this area has been focused on improving a model’s performance with the assumption that the preprocessed data are clean. Here, we studied how to remove noise from raw knowledge graphs with limited labeled information. Methods The proposed framework used generative-based deep neural networks to generate a graph that can distinguish the unknown associations in the raw training graph. Two generative adversarial network models, NetGAN and Cross-Entropy Low-rank Logits (CELL), were adopted for the edge classification (ie, link prediction), leveraging unlabeled link information based on a real knowledge graph built from LitCovid and Pubtator. Results The performance of link prediction, especially in the extreme case of training data versus test data at a ratio of 1:9, demonstrated that the proposed method still achieved favorable results (area under the receiver operating characteristic curve >0.8 for the synthetic data set and 0.7 for the real data set), despite the limited amount of testing data available. Conclusions Our preliminary findings showed the proposed framework achieved promising results for removing noise during data preprocessing of the biomedical knowledge graph, potentially improving the performance of downstream applications by providing cleaner data.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation

Jiang¹,

Ngo²,

Chapman³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Other works in graph drawing chose to visually reveal these properties in order to facilitate their exploratory analysis [Brandes11,Raj17]. For example, in graph analytics, some works are interested in understanding and describing the interaction structure by analyzing the topology of the graph [Saqr18,Elmouden20]. Others are interested in identifying and characterizing the nodes that are particularly important in terms of topological position in a graph [Wang17] and how their neighbors are connected to each other [Zhang17].…”

Section: Introductionmentioning

confidence: 99%

Human-centered Evaluation of 3D Radial Layouts for Centrality Visualization

Kobina¹,

Brisson²,

David³

et al. 2022

CSRN

View full text Add to dashboard Cite

In this paper we propose improvements to the 3D radial layouts that make it possible to visualize centrality mea- sures of the nodes in a graph. Our improvements mainly relate edge drawing and the evaluation of the 3D radial layouts. First, we projected not only the nodes but also the edges onto the visualization surfaces in order to reduce the node overlap that could be observed in previous 3D radial layouts. Secondly, we proposed a human-centered evaluation in order to compare the efficiency score and the time to complete tasks of the 3D radial layouts to those of the 2D radial layouts. The evaluation tasks proposed are related to the central nodes, the peripheral nodes and the dense areas of a graph. The results showed that 3D layouts can perform significantly better than 2D layouts in terms of efficiency when tasks are related to the central and peripheral nodes, while the difference in time is not statistically significant between these various layouts. Additionally, we found that the participants preferred interacting with 3D layouts over 2D layouts.

show abstract

“…A knowledge base built upon the integration of biomedical entities from such a literature hub would provide tremendous value in the exploration of explicit or implicit associations among diverse biomedical entities as investigators attempt to answer clinical questions related to COVID-19. A number of recently published journal articles have included graph-based analysis of COVID-19 data sets [3]. For example, Groza [4] analyzed how a semantically annotated data set would be helpful in detecting and preventing potentially harmful misinformation regarding the spread of COVID-19 based on CORD-19-on-FHIR (a linked data version of the COVID-19 Open Research Dataset data represented in FHIR RDF by mining the CORD-19 data set and adding semantic annotations) [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation (Preprint)

Jiang¹,

Ngo²,

Chapman³

et al. 2022

Preprint

View full text Add to dashboard Cite

BACKGROUND Multiple types of biomedical associations of knowledge graphs, including COVID-19–related ones, are constructed based on co-occurring biomedical entities retrieved from recent literature. However, the applications derived from these raw graphs (eg, association predictions among genes, drugs, and diseases) have a high probability of false-positive predictions as co-occurrences in the literature do not always mean there is a true biomedical association between two entities. OBJECTIVE Data quality plays an important role in training deep neural network models; however, most of the current work in this area has been focused on improving a model’s performance with the assumption that the preprocessed data are clean. Here, we studied how to remove noise from raw knowledge graphs with limited labeled information. METHODS The proposed framework used generative-based deep neural networks to generate a graph that can distinguish the unknown associations in the raw training graph. Two generative adversarial network models, NetGAN and Cross-Entropy Low-rank Logits (CELL), were adopted for the edge classification (ie, link prediction), leveraging unlabeled link information based on a real knowledge graph built from LitCovid and Pubtator. RESULTS The performance of link prediction, especially in the extreme case of training data versus test data at a ratio of 1:9, demonstrated that the proposed method still achieved favorable results (area under the receiver operating characteristic curve >0.8 for the synthetic data set and 0.7 for the real data set), despite the limited amount of testing data available. CONCLUSIONS Our preliminary findings showed the proposed framework achieved promising results for removing noise during data preprocessing of the biomedical knowledge graph, potentially improving the performance of downstream applications by providing cleaner data.

show abstract

Towards Using Graph Analytics for Tracking Covid-19

Cited by 11 publications

References 25 publications

Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation

Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation

Human-centered Evaluation of 3D Radial Layouts for Centrality Visualization

Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation (Preprint)

Contact Info

Product

Resources

About