Is there an underlying link between COVID-19, ACE2, oxytocin and vitamin D?

Diep, Phuoc-Tan

doi:10.1016/j.mehy.2020.110360

“…AGATHA-C was tested whether it will be able to predict compounds potentially applicable for the treatment of COVID-19 and the genes involved in the SARS-CoV-2 pathogenesis. The data confirming cardiovascular protective effects of hormone oxytocine were published recently [9,40]. The protective effect is linked to anti inflammatory activity of the hormone.…”

Section: Case Studymentioning

confidence: 69%

Accelerating COVID-19 research with graph mining and transformer-based learning

Tyagin

¹

,

Kulshrestha

²

,

Sybrandt

³

et al. 2021

Preprint

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In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on graph-mining and the transformer model. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone.

show abstract

“…AGATHA-C was tested whether it would be able to predict compounds potentially applicable for the treatment of COVID-19 and the genes involved in the SARS-CoV-2 pathogenesis. The data confirming cardiovascular protective effects of hormone oxytocine were published recently (Diep 2021;Wang and Wang 2021). The protective effect is linked to anti-inflammatory activity of the hormone.…”

Section: Emergent Discovery Case Studymentioning

confidence: 70%

Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning

Tyagin

¹

,

Kulshrestha

²

,

Sybrandt

³

et al. 2022

AAAI

View full text Add to dashboard Cite

In 2020, the White House released the “Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset,” wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on the graph mining and transformer models. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover ongoing research findings such as the relationship between COVID-19 and oxytocin hormone. All code, details, and pre-trained models are available at https://github.com/IlyaTyagin/AGATHA-C-GP.

show abstract

“…AGATHA-C was tested whether it will be able to predict compounds potentially applicable for the treatment of COVID-19 and the genes involved in the SARS-CoV-2 pathogenesis. The data confirming cardiovascular protective effects of hormone oxytocine were published recently [9,40]. The protective effect is linked to Table 2: Classification and recommendation quality metrics across recently popular COVID-19-related biomedical subdomains.…”

Section: Case Studymentioning

confidence: 83%

Accelerating COVID-19 research with graph mining and transformer-based learning

Tyagin¹,

Kulshrestha²,

Sybrandt³

et al. 2021

Preprint

View full text Add to dashboard Cite

In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on graph-mining and the transformer model. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone.

show abstract

Is there an underlying link between COVID-19, ACE2, oxytocin and vitamin D?

Cited by 10 publications

References 6 publications

Accelerating COVID-19 research with graph mining and transformer-based learning

Accelerating COVID-19 research with graph mining and transformer-based learning

Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning

Accelerating COVID-19 research with graph mining and transformer-based learning

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