Thyroid Hormone Levels in Apparently Euthyroid Subjects with Essential Hypertension in a Tertiary Hospital in Nigeria

Emokpae, Abiodun Mathias; Abdu, Aliyu; Osadolor, Humphrey B.

doi:10.4103/0974-2727.115921

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The relatedness of diseases belonging to different top-level DO categories was also identified, e.g., essential hypertension (DOID: 10825) & hyperthyroidism (DOID: 7998). Recently, Emokpae et al had pointed out that hyperthyroidism was the most common thyroid disorder observed in patients with essential hypertension [ 50 ]. It indicates that our method can recognize related diseases which apparently seem unrelated.…”

Section: Resultsmentioning

confidence: 99%

Fusing literature and full network data improves disease similarity computation

Nie

2016

BMC Bioinformatics

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BackgroundIdentifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature.ResultsAmong function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively.ConclusionsIntegrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http://www.digintelli.com:8000/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1205-4) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%