Narrow Results

Collection and synthesis of evidence is a key task in the development of the simulation models required for health technology assessment (HTA). The implementation of some of these models, such as discrete event simulation (DES) models, presents technical difficulties and requires higher technical skills. This work presents a method to extract the knowledge stored in an ontology, Rare Disease Ontology for Simulation (RaDiOS), to generate a DES model. RaDiOS is a domain ontology focused on collecting evidence on rare diseases for simulation models. We reviewed and enhanced the ontology to increase its semantic expressiveness. Besides, we developed a transformation tool (RaDiOS-MTT) to automatically generate DES models from the knowledge stored in the ontology. We defined a set of “synthetic” diseases, with simple natural histories, represented them in RaDiOS, and compared the results of the automatically generated simulation models with their manually created counterparts. Afterwards, we used a case study on a real intervention (newborn screening for profound biotinidase deficiency) to validate our approach. The automatically generated models for the synthetic diseases mimicked their programmatic counterparts in structure and results. The same happened to the model for profound biotinidase deficiency.

AUSTRALIA – Cynata achieves major stem cell manufacturing milestone.

INDONESIA – Rice crucial to Indonesia's food security.

JAPAN – Japan joins Gates's Grand Challenge on tropical diseases.

JAPAN – On-farm system turns rice plants into biofuel and fodder.

SINGAPORE – Infant gut microbiota linked with gestation duration, delivery method and healthy weight gain.

SINGAPORE – Mundipharma demonstrates efficacy of BETADINE(R) formulations against Ebola virus.

SINGAPORE – Scientists determine mechanical forces that drive epithelial wound healing.

SINGAPORE – A*STAR researchers develop expert systems for identifying treatment targets for cancer and rare diseases.

SINGAPORE – NUS researchers pioneer novel strategy to prevent progression of inflammation-associated cancers.

THE PHILIPPINES – New biotech hub aims to nurture more rice researchers.

AFRICA – NAU researcher works to understand forces of abrupt climate change.

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AFRICA – Experts call for innovations to aid Africa's healthcare.

EUROPE – Europe's first fully remote diabetes trial approved.

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ASEAN+ Rare Disease Network Established.

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NUS Pharmacy Team Develops Online “Calculator” to Predict Risk of Early Hospital Readmission.

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AstraZeneca’s CVD-REAL Study Shows SGLT-2 Inhibitors Significantly Reduced Death and Hospitalisations for Heart Failure Versus Other Type-2 Diabetes Medicines.

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ASEAN⁺ Rare Disease Network: Unifying Voices of Patients.

Rainbow Across Borders.

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Drugs to watch 2018

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AI and blockchain will counteract unsustainability of soaring drug prices

Availability of orphan medicines varies between European countries

Millions of Americans are affected by rare diseases, many of which have poor survival rates. However, the small market size of individual rare diseases, combined with the time and capital requirements of pharmaceutical R&D, have hindered the development of new drugs for these cases. A promising alternative is drug repurposing, whereby existing FDA-approved drugs might be used to treat diseases different from their original indications. In order to generate drug repurposing hypotheses in a systematic and comprehensive fashion, it is essential to integrate information from across the literature of pharmacology, genetics, and pathology. To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). GNBR is a large, heterogeneous knowledge graph comprising drug, disease, and gene (or protein) entities linked by a small set of semantic themes derived from the abstracts of biomedical literature. We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. This approach achieves high performance on a gold-standard test set of known drug indications (AUROC = 0.89) and is capable of generating novel repurposing hypotheses, which we independently validate using external literature sources and protein interaction networks. Finally, we demonstrate the ability of our model to produce explanations of its predictions.