McKusick's Online Mendelian Inheritance in Man (OMIM(R))

Amberger, Joanna S.; Bocchini, Carol; Scott, Alan F.; Hamosh, Ada

doi:10.1093/nar/gkn665

Cited by 643 publications

(574 citation statements)

References 3 publications

(3 reference statements)

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“…The similarities in this matrix were calculated based on various text mining algorithms on OMIM records, which describe diseases using natural language [33]. By selecting only five neighbors which have largest similarities for each node, we constructed a phenotypic disease similarity network (shortly called OMIMNet) consisting of 19,791 interactions among 5080 phenotypes.…”

Section: Omim-based Disease Similarity Networkmentioning

confidence: 99%

“…AD is a multi-factorial and fatal neurodegenerative disorder for which the mechanisms leading to profound neuronal loss are incompletely recognized. There are 16 genes are known to be associated with AD [33]; however only eleven of them are available in the gene/protein networks. To predict novel genes associated with this disease, we selected the heterogeneous network comprising HPONet and GENet.…”

Section: Case Study: Alzheimer's Diseasementioning

confidence: 99%

“…This algorithm was then applied to predict disease-associated genes on a heterogeneous network of proteins and disease phenotypes [32]. This network was constructed by integrating a disease similarity network based on text mining algorithms on OMIM records [33] and a protein interaction network. As a result, it was reported that RWRH better exploit "disease module" principle than RWR [22] since then OMIM-based disease similarity network was additionally integrated [32].…”

Section: Introductionmentioning

confidence: 99%

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Ontology-based disease similarity network for disease gene prediction

Dang²

2016

Vietnam J Comput Sci

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Finding underlying molecular mechanisms of diseases is one of the important issues in biomedical research. In which, prediction of novel disease-associated genes is mostly focused. Many methods have been proposed based on biological networks and shown effectively for the problem. These network-based methods are usually relied on a "disease module" principle that functionally similar genes are associated with similar phenotypes or diseases. Among them, methods solely based on gene/protein networks only exploit that principle by structural modules in the gene/protein networks. Meanwhile, others based on integration of these networks with a disease similarity network better exploit the principle and consequently result in higher prediction performance. In these studies, the disease similarity network is extracted from a disease similarity matrix which was calculated using text mining techniques on OMIM records. Considering that diseases have been recently well annotated by human phenotype ontology (i.e., a controlled vocabulary database) and semantic similarity measures can be used to calculate similarities among them. Therefore, it would be more accurate to construct disease similarity network based on semantic similarity measures on phenotype ontol- Experiment results show that the ontology-based disease similarity network much improves the prediction performance compared to the one based on OMIM records, irrespective of gene/protein networks. In addition, we show ability of our method in predicting novel Alzheimer's disease-associated genes, in which 19 out of top 100 ranked candidate genes are supported with evidences from literature.

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Section: Omim-based Disease Similarity Networkmentioning

confidence: 99%

Section: Case Study: Alzheimer's Diseasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ontology-based disease similarity network for disease gene prediction

Dang²

2016

Vietnam J Comput Sci

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“…Đây là một mạng vô hƣớng, có trọng số (biểu thị độ tƣơng tự về chức năng giữa các gen/protein) gồm 11.886 gen và 111.943 liên kết. Thêm vào đó, chúng tôi sử dụng các cơ sở dữ liệu về bệnh và các gen liên quan đã biết từ OMIM [18]. Kết quả thu đƣợc 622 bệnh với t ng số 3246 gen liên quan, trong đó 148 bệnh có từ 2 gen liên quan trở lên đã đƣợc phát hiện.…”

Section: A Dữ Liệuunclassified

Một Phương Pháp Cải Tiến Cho Bài Toán Xác Định Các Gen Liên Quan Đến Bệnh

Phong¹,

Tùng²,

Hậu³

et al. 2017

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin - 2016

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“…1A). Important links included HGMD (55.6%), OMIM (Online Mendelian Inheritance in Man) [Amberger et al, 2009] (76.5%) and other useful links (91.2%; GenBank/EMBL, HGVS, etc. ), but only 0.3% included links to other LSDBs.…”

Section: Database Analysismentioning

confidence: 99%

Locus-specific database domain and data content analysis: evolution and content maturation toward clinical usea

et al. 2010

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Genetic variation databases have become indispensable in many areas of health care. In addition, more and more experts are depositing published and unpublished disease-causing variants of particular genes into locus-specific databases (LSDBs). Some of these databases contain such extensive information that they have become known as knowledge bases. Here, we analyzed 1,188 LSDBs and their content for the presence or absence of 44 content criteria related to database features (general presentation, locus-specific information, database structure) and data content (data collection, summary table of variants, database querying). Our analyses revealed that several elements have helped to advance the field and reduce data heterogeneity, such as the development of specialized database management systems and the creation of data querying tools. We also identified a number of deficiencies, namely, the lack of detailed disease and phenotypic descriptions for each genetic variant and links to relevant patient organizations, which, if addressed, would allow LSDBs to better serve the clinical genetics community. We propose a structure, based on LSDBs and closely related repositories (namely, clinical genetics databases), which would contribute to a federated genetic variation browser and also allow the maintenance of variation data.

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McKusick's Online Mendelian Inheritance in Man (OMIM(R))

Cited by 643 publications

References 3 publications

Ontology-based disease similarity network for disease gene prediction

Ontology-based disease similarity network for disease gene prediction

Một Phương Pháp Cải Tiến Cho Bài Toán Xác Định Các Gen Liên Quan Đến Bệnh

Locus-specific database domain and data content analysis: evolution and content maturation toward clinical usea

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