SIDD: A Semantically Integrated Database towards a Global View of Human Disease

Liang, Cheng; Wang, Guohua; Li, Jie; Zhang, Tianjiao; Xu, Peigang; Wang, Yadong

doi:10.1371/journal.pone.0075504

Cited by 44 publications

(26 citation statements)

References 37 publications

(45 reference statements)

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“…To get the disease-related genes, we downloaded the supplementary files of [57] which contains 117,190 associations between 2817 diseases and 12063 genes from the database SIDD [58]. After data correction and redundancy removal, we obtained a data set of 114754 disease-gene associations between 2802 diseases and 10893 genes.…”

Section: Methodsmentioning

confidence: 99%

Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

et al. 2017

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BackgroundMicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics.Methods and resultsWe designed a computational method to detect multi-disease associated co-functional microRNA pairs and conducted cross disease analysis on a reconstructed disease-gene-microRNA (DGR) tripartite network. The construction of the DGR tripartite network is by the integration of newly predicted disease-microRNA associations with those relationships of diseases, microRNAs and genes maintained by existing databases. The prediction method uses a set of reliable negative samples of disease-microRNA association and a pre-computed kernel matrix instead of kernel functions. From this reconstructed DGR tripartite network, multi-disease associated co-functional microRNA pairs are detected together with their common dysfunctional target genes and ranked by a novel scoring method. We also conducted proof-of-concept case studies on cancer-related co-functional microRNA pairs as well as on non-cancer disease-related microRNA pairs.ConclusionsWith the prioritization of the co-functional microRNAs that relate to a series of diseases, we found that the co-function phenomenon is not unusual. We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1605-0) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

et al. 2017

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“…We downloaded disease-gene associations from the supplementary file of a published article [ 18 ] which contains 117,190 associations between 2817 diseases and 12063 genes. The authors collected these data records from database SIDD [ 39 ]. Each of the diseases has a unique id from database DO.…”

Section: Methodsmentioning

confidence: 99%

Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes

et al. 2017

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Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.

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“…According to Seyfang et al [ 13 ] and Isern et al [ 14 ], ontologies can be developed to represent formal guidelines. Cheng et al [ 15 ] have also made progress in establishing semantic associations among disease related databases to provide a more global view of human diseases.…”

Section: Related Workmentioning

confidence: 99%

A method for exploring implicit concept relatedness in biomedical knowledge network

et al. 2016

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BackgroundBiomedical information and knowledge, structural and non-structural, stored in different repositories can be semantically connected to form a hybrid knowledge network. How to compute relatedness between concepts and discover valuable but implicit information or knowledge from it effectively and efficiently is of paramount importance for precision medicine, and a major challenge facing the biomedical research community.ResultsIn this study, a hybrid biomedical knowledge network is constructed by linking concepts across multiple biomedical ontologies as well as non-structural biomedical knowledge sources. To discover implicit relatedness between concepts in ontologies for which potentially valuable relationships (implicit knowledge) may exist, we developed a Multi-Ontology Relatedness Model (MORM) within the knowledge network, for which a relatedness network (RN) is defined and computed across multiple ontologies using a formal inference mechanism of set-theoretic operations. Semantic constraints are designed and implemented to prune the search space of the relatedness network.ConclusionsExperiments to test examples of several biomedical applications have been carried out, and the evaluation of the results showed an encouraging potential of the proposed approach to biomedical knowledge discovery.

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SIDD: A Semantically Integrated Database towards a Global View of Human Disease

Cited by 44 publications

References 37 publications

Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes

A method for exploring implicit concept relatedness in biomedical knowledge network

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