The disease and gene annotations (DGA): an annotation resource for human disease

Peng, Kai; Xu, Wei; Zheng, Jun; Huang, Kegui; Wang, Huisong; Jiao, Tong; Lin, Zhifeng; Li, Jun; Cheng, Wenqing; Fu, Dong Jing; Du, Pan; Kibbe, Warren A.; Lin, Simon; Xia, Tian

doi:10.1093/nar/gks1244

Cited by 57 publications

(49 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We then obtained disease to gene associations for the same set of ICD9 codes from the MalaCards [29] and Disease Ontology [28] datasets which are semi-automatically curated resources with disease-genotype information. MalaCards integrates various disease annotations (e.g.…”

Section: Benchmarking the Annotations Resulting From Integrated Ontolmentioning

confidence: 99%

“…Such predicted ICD9 to gene annotations come from transferring the gene annotations to ICD9 codes via GO IDs based on the integrated ontology on the UMLS. This data was used for evaluation of the merging process by studying enrichments comparing with the disease to gene annotations currently available from Disease Ontology (DO) [28] and MalaCards [29] databases, as they form two domain specific high-throughput datasets currently available for studying disease to gene annotations. To perform enrichments, we computed the overlap between the annotated ICD9 to gene associations from these resources and those discovered by our approach to calculate the hyper geometric probability of the overlap.…”

Section: Evaluation Of the Integration Of Icd9 And Go To Study The Efmentioning

confidence: 99%

See 1 more Smart Citation

A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies

Hashemikhabir

Xia

Xiang

et al. 2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

Abstract-Although some methods are proposed for automatic ontology generation, none of them address the issue of integrating large-scale heterogeneous biomedical ontologies. We propose a novel approach for integrating various types of ontologies efficiently and apply it to integrate International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9CM) and Gene Ontologies (GO). This approach is one of the early attempts to quantify the associations among clinical terms (e.g. ICD9 codes) based on their corresponding genomic relationships. We reconstructed a merged tree for a partial set of GO and ICD9 codes and measured the performance of this tree in terms of associations' relevance by comparing them with two well-known disease-gene datasets (i.e. MalaCards and Disease Ontology). Furthermore, we compared the genomic-based ICD9 associations to temporal relationships between them from electronic health records. Our analysis shows promising associations supported by both comparisons suggesting a high reliability. We also manually analyzed several significant associations and found promising support from literature.

show abstract

Section: Benchmarking the Annotations Resulting From Integrated Ontolmentioning

confidence: 99%

Section: Evaluation Of the Integration Of Icd9 And Go To Study The Efmentioning

confidence: 99%

A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies

Hashemikhabir

Xia

Xiang

et al. 2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

show abstract

“…The Disease and Gene Annotations database (DGA) (Peng, et al, 2013) . A significance score of 10 -5 was used as a cut-off value for inclusion in the list of candidate genes.…”

Section: Database Mining and Network Analysismentioning

confidence: 99%

“…We next explored the DGA interface (Peng, et al, 2013) and collected a group of 20 genes associated with PD and T2DM in addition to the set of genes found in DisGeNET (Supplementary Table 1). Genes involved in insulin signaling including AKT1, IGF1, and E2F1 were present in this database.…”

Section: Shared Susceptibility Genes In Pd and T2dmmentioning

confidence: 99%

Splice Variant Biomarkers for Parkinson's Disease

Potashkin¹

2014

View full text Add to dashboard Cite

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATEMay PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) AND ADDRESS(ES) PERFORMING ORGANIZATION REPORT NUMBERRosalind Franklin U. Medicine & Science 3333 Green Bay Rd. North Chicago, IL 60064-3037 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S) U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland 21702-5012 SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION / AVAILABILITY STATEMENTApproved for Public Release; Distribution Unlimited SUPPLEMENTARY NOTES ABSTRACTRecently we identified splice variants in whole blood than can distinguish early stage Parkinson's disease (PD) patients from healthy and neurodegenerative controls. The proposed studies will test the hypothesis that the biomarkers will be useful for identifying individuals at risk for PD and for identifying signaling pathways that are disrupted in PD. We are testing the expression of the biomarkers in RNA prepared from whole blood of hyposmic participants in the Parkinson's Associated Risk Study (PARS) (Technical Objective 1.0). In order to establish a model for testing the function of the biomarkers, we are determining whether their expression is altered in human olfactory neurosphere-derived (hONS) cells derived from idiopathic PD patients and healthy controls (Technical Objective 2.0). To determine the signaling pathways affected in PD and identify additional biomarkers, we are using network analysis (Technical Objective 3.0). During year 1, RNA and cDNA was prepared from PARS samples from Year 0 (baseline) and Year 2. Quantitative polymerase chain reactions were initiated for two of the biomarkers (APP, HNF4α). Expression of the biomarkers was tested in hONS. We also developed network approaches to reveal the common molecular pathways involved with PD and type 2 diabetes (T2DM). Using these networks, we identified APP, HNF4A and SOD2 mRNAs as blood biomarkers predictive of early stage PD. In addition, we determined that HNF4A mRNA may be a progression marker in blood for PD. SUBJECT TERMSParkinson's disease, biomarkers, neurodegeneration, network and pathway analysis 12-15Santiago 16-23Sei...

show abstract

“…Other genes in the cluster shared GO terms and disease involvement including "hepatitis B susceptibility", "retinal degeneration", "cholesterol transport", as listed in Table 4. Thus, we further explored the disease implications of each cluster of SNPs using GeneAnswers [8] and Disease Ontology annotation of the human genome [22]. The results are illustrated in Table 5.…”

Section: Identified Snp-associative Gene Relationsmentioning

confidence: 99%

Mining massive SNP data for identifying associated SNPs and uncovering gene relationships

Webb

Albin

et al. 2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

View full text Add to dashboard Cite

Studies on SNP correlations have been focused on SNPs located on the same chromosome since SNPs on different chromosomes are expected to segregate randomly. Previous studies suggest that SNPs can be associated with each other over long distances and even across different chromosomes. To facilitate the study of SNP associations, our goal is to find SNPs that coexist in a significant number of samples regardless of their genomic distance, and subsequently to study the relationships among these associated SNPs and corresponding genes. This problem of mining co-occurrent SNP associations is computationally challenging and motivates us to design an efficient data mining algorithm FCI-RC to mine SNP associations from massive SNP data. By applying our method on the original SNP data and random chromosome permutation data, we demonstrate that our method is able to find non-random SNP associations across multiple chromosomes. Among the large amount of associated SNPs identified by our method, many of them involve multiple chromosomes. Some SNP associations also suggest novel relationships among the corresponding genes, and some may imply biological and disease mechanisms related to corresponding genes.

show abstract

The disease and gene annotations (DGA): an annotation resource for human disease

Cited by 57 publications

References 26 publications

A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies

A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies

Splice Variant Biomarkers for Parkinson's Disease

Mining massive SNP data for identifying associated SNPs and uncovering gene relationships

Contact Info

Product

Resources

About