Linked Life Data sequel is here to dish out more value to life science research
June 30, 2011
Keeping the continuous quality improvement of the public Linked Life Data (LLD) service, the 0.7 was brought to the public today.
“We have spent a lot of time and resources in order to define more flexible and manageable update process methodology” - says Todor Primov of the LLD team – “With the current release of Linked Life Data public service, we have introduced the new data update process and have proved its readiness to provide quality of our Data as a Service (DaaS) based solution. The new LLD update routines will allow us to speed up the delivery of LLD regular updates and more flexibility in the integration of new data sources. Another important achievement for this Linked Life Data release is the significant improvement of the quality of the instance mappings and the inferred causal relations - we have significantly reduces the number of the false positive mappings and relations!”
A public RDF warehouse service to semantically integrate 27 diverse data sources, LLD currently congregates 1,028,154,006 entities, 4,650,877,794 explicit statements and 5,120,886,447 statements in total.
New functionality and improvements in the 0.7 release:
- Update of all primary data sets, which have new releases.
- Described a new data set - MetaCyc (as part of BioPax).
- New instance mapping of MeSH codes in LHGDN data set to UMLS concepts.
- New instance mapping of Freebase concepts to UMLS concepts.
- Improved instance mappings between BioPax participants (protein/gene) and UniProt/EntrezGene entities. Now we distinguish unification relations (as exactMatch) from reference relations (as closeMatch) between the instances from the two data sets.
- The entire set of PubMed articles were annotated with the new Semantic Biomedical Tagger v.1.1 (SBM 1.1). The annotation process generated more than half a billion high quality semantic annotations with concepts from more than 130 semantic types.
- Improved generation (false positives removed) of causality relations between 14 different biomedical entities (Genes, Protein, Diseases, Symptoms, Drugs, Side Effects, Biological Processes, Molecular Functions, Cellular Localizations, Organisms, Organs, Cell Types, Cell Lines). Causality relations include relations of the following types:
|
Entity_Type_1 |
Causality_Relation_Type |
Entity_Type_2 |
#_of_causality_relations |
|
Disease |
hasSymptom |
UMLS concept |
35505 |
|
Drug |
hasSideEffect |
UMLS concept |
450082 |
|
Drug |
hasTarget |
Protein |
15309 |
|
Drug |
treat |
Disease |
8201 |
|
Gene |
encode |
Protein |
6033749 |
|
Protein |
expressedInOrganism |
UMLS concept |
22652496 |
|
Protein |
expressedInAnatomicalSystem |
UniProt keyword |
275824 |
|
Protein |
expressedInCellLine |
UniProt keyword |
16912 |
|
Protein |
expressedInCellType |
UniProt keyword |
12728 |
|
Protein |
binding |
Protein |
68150720 |
|
Protein |
hasLocalization |
GO term |
5878896 |
|
Protein |
hasMolecularFunction |
GO term |
13981106 |
|
Protein |
participateInBiologicalProcess |
GO term |
7885200 |
|
ClinicalTrial |
hasInclusionCriteria |
UMLS concept |
420797 |
|
ClinicalTrial |
hasExclusionCriteria |
UMLS concept |
844390 |
|
TOTAL |
126661915 |
- Auto-complete index limited to UMLS concepts, LOD drugs and human proteins/genes
- A new configuration of RelFinder tuned for exploring the causality relations in LLD
- A set of 3 new examples to explore causal relations with RelFinder
- Added 2 new example SPARQL queries combining causal relations and co-occurrence of concepts in unstructured texts.
- Minor fixes of LLD data and end user interface.
The service is accessible at http://linkedlifedata.com
Ontotext is a Sirma Group company
