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EFO / Experimental Factor Ontology
An application ontology driven by the annotation and query needs of samples in omics datasets. EFO provides an integration framework for ontologies and combines parts of several biological ontologies such as UBERON anatomy, ChEBI chemical compounds, and Cell Ontology. The relationship between ontology descriptions of rare and common diseases and their phenotypes can offer insights into shared biological mechanisms and potential drug targets. The scope of EFO is to support the annotation, analysis and visualization of several domain specific ontologies.
Utilizes simple yet effective linguistic features to extract relations with maximum entropy models. This chemical-induced disease (CID) relation extraction tool consisted of two specific subtasks: (i) the primary step for automatic Chemical Disease Relation (CDR) extraction is disease named entity recognition and normalization (DNER); (ii) CID relation extraction. Participants were provided with the same raw text as DNER, and asked to return a ranked list of chemical and disease entity pairs with normalized concept identifiers with which CIDs were associated in the abstract.
A system for disease mention recognition and normalization in biomedical texts. AuDis utilizes an order two conditional random fields model. To optimize the results, we customize several post-processing steps, including abbreviation resolution, consistency improvement and stopwords filtering. As the official evaluation on the chemical disease relation (CDR) task in BioCreative V, it obtained the best performance (86.46% of F-score) among 40 runs on disease normalization of the disease named entity recognition (DNER) sub task. These results suggest that AuDis is a high-performance recognition system.
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NCBI disease corpus
A collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations.
Allows automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. DiseaseExtract includes two broad stages: (i) extracting disease mentions from free text–a task referred to as disease named entity recognition (DNER) and (ii) normalizing the recognized mentions to standard controlled vocabularies such as MeSH–a task referred to as disease name normalization (DNORM).
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