A5015791072- Biomedical Text Mining and Ontologies
- Bioinformatics and Genomic Networks
- Genomics and Rare Diseases
- Genetics, Bioinformatics, and Biomedical Research
- Single-cell and spatial transcriptomics
- Research Data Management Practices
- Gene expression and cancer classification
- Semantic Web and Ontologies
- Cell Image Analysis Techniques
- Genomics and Phylogenetic Studies
- Scientific Computing and Data Management
- AI in cancer detection
Bayer (Germany)
2019
European Bioinformatics Institute
2013-2016
Wellcome Trust
2014-2016
The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands utility ontology for examination and comparison genetic variation, phenotype, protein, drug epitope data through lens human disease. DO is a biomedical resource standardized common rare disease concepts with stable identifiers organized by etiology. content has had 192 revisions since 2012, including addition 760 terms. Thirty-two percent all terms now include definitions. expanded...
The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available common disease. Here, we have developed concept-recognition procedure that analyzes frequencies HPO annotations as identified over five million PubMed abstracts by employing an iterative to optimize precision recall terms. We derived...
Biopharmaceutical industry R&D, and indeed other life sciences R&D such as biomedical, environmental, agricultural food production, is becoming increasingly data-driven can significantly improve its efficiency effectiveness by implementing the FAIR (findable, accessible, interoperable, reusable) guiding principles for scientific data management stewardship. By so doing, plethora of new powerful analytical tools artificial intelligence machine learning will be able, automatically at scale, to...
The Centre for Therapeutic Target Validation (CTTV - https://www.targetvalidation.org/ ) was established to generate therapeutic target evidence from genome-scale experiments and analyses. CTTV aims support the validity of targets by integrating existing newly-generated data. Data integration has been achieved in some resources mapping metadata such as disease phenotypes Experimental Factor Ontology (EFO). Additionally, relationship between ontology descriptions rare common diseases their...
The BioSamples database at the EBI (http://www.ebi.ac.uk/biosamples) provides an integration point for information between technology specific databases EBI, projects such as ENCODE and reference collections cell lines. delivers a unified query interface API to sample across EBI's links back assay databases. Sample groups are used manage related samples, e.g. those from experimental submission, or single collection. Infrastructural improvements include new user with ontological key word...