DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

Piñero, Janet; Queralt-Rosiñach, Núria; Bravo, Àlex; Deu-Pons, Jordi; Bauer-Mehren, Anna; Baron, Martín; Sanz, Ferrán; Furlong, Laura I.

doi:10.1093/database/bav028

Cited by 885 publications

(731 citation statements)

References 48 publications

(52 reference statements)

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“…In order to evaluate the comprehensiveness of the gene list, we got that with "Joubert syndrome" as the keyword from two published websites including DisGeNET [16,17] and DISEASES [18], both of which are comprehensive platform integrating information on human disease-associated genes and variants. We got 19 and 15 genes related to Joubert syndrome from DisGeNET and DISEASES, respectively.…”

Section: Resultsmentioning

confidence: 99%

VarfromPDB: An Automated and Integrated Tool to Mine Disease-Gene-Variant Relations from the Public Databases and Literature

Cao¹,

Wang²,

Chen³

et al. 2017

J Proteomics Bioinform

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Section: Resultsmentioning

confidence: 99%

VarfromPDB: An Automated and Integrated Tool to Mine Disease-Gene-Variant Relations from the Public Databases and Literature

Cao¹,

Wang²,

Chen³

et al. 2017

J Proteomics Bioinform

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“…In addition, the top 30 reported GBM associated genes were obtained from DisGeNET database (26). The protein-protein interaction data was obtained from the BioGRID database (28) as the background signaling network, and the self-interaction edges were removed.…”

Section: Network-based Drug Repositioningmentioning

confidence: 99%

Computational Approaches and Pharmacogenomics Data Resources for Drug Repositioning

2017

MRAJ

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Large-scale data sets of cancer patients have been being generated due to the significantly reduced cost of sequencing full genome of individual patients using the Next Generation Sequencing (NGS) technology. Comprehensive genomics data analysis revealed the diverse dysfunctional biomarkers of individual cancer patients, which are believed to be responsible for heterogeneous drug response. Thus precision medicine is becoming popular that aims to find the optimal treatments for individual patients based on their genomics profiling data. However, it is challenging to interpret the complicated and distinct genome mutation and variation patterns, and associate them to optimal treatments. Though a set of approaches and data resources have been reported to reposition FDA approved drugs and investigational drugs for specific diseases, novel and sophisticated computational approaches are needed urgently to reposition drugs for cancer subtypes or individual patients. In this study, some widely used computational approaches and pharmacogenomics data resources for repositioning optimal drugs are introduced and discussed, which aims to provide a general overview of the genomic data-driven drug repositioning, and help readers understand the topic conveniently.

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“…As shown in figure 1b, in the molecular diseasome two diseases are linked if they: 1) share disease-associated genes, as identified by DisGeNET, a database that integrates information on gene−disease associations from various public repositories and the biomedical literature [21,22]; and/or 2) the proteins encoded by disease-associated genes are connected in the interactome [23], a publically available protein interaction network (HIPPIE) [24]. As detailed in the online supplement, to reduce the potential bias that shared genes/proteins might be more easily identified in those diseases that have been more extensively characterised, and to estimate the strength of the association between two diseases in the molecular diseasome, we used the Molecular Comorbidity Index (MCI) [13] and a bootstrap analysis.…”

Section: Molecular Diseasomementioning

confidence: 99%

Molecular and clinical diseasome of comorbidities in exacerbated COPD patients

et al. 2015

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The frequent occurrence of comorbidities in patients with chronic obstructive pulmonary disease (COPD) suggests that they may share pathobiological processes and/or risk factors.To explore these possibilities we compared the clinical diseasome and the molecular diseasome of 5447 COPD patients hospitalised because of an exacerbation of the disease. The clinical diseasome is a network representation of the relationships between diseases, in which diseases are connected if they co-occur more than expected at random; in the molecular diseasome, diseases are linked if they share associated genes or interaction between proteins.The results showed that about half of the disease pairs identified in the clinical diseasome had a biological counterpart in the molecular diseasome, particularly those related to inflammation and vascular tone regulation. Interestingly, the clinical diseasome of these patients appears independent of age, cumulative smoking exposure or severity of airflow limitation.These results support the existence of shared molecular mechanisms among comorbidities in COPD.@ERSpublications Half of the comorbidities observed in COPD patients hospitalised by an exacerbation share common molecular mechanisms

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DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

Cited by 885 publications

References 48 publications

VarfromPDB: An Automated and Integrated Tool to Mine Disease-Gene-Variant Relations from the Public Databases and Literature

VarfromPDB: An Automated and Integrated Tool to Mine Disease-Gene-Variant Relations from the Public Databases and Literature

Computational Approaches and Pharmacogenomics Data Resources for Drug Repositioning

Molecular and clinical diseasome of comorbidities in exacerbated COPD patients

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