Frequently Asked Questions
What is Translational Science?
Translational Science is the art of applying knowledge and insights from one scientific domain to another. An example is the application of insights gained in the lab using model organisms to patient treatments in the clinic. Key enabling technologies in translational science are ontologies, knowledge graphs, and data standards.
What is an ontology?
An ontology is a formal, computational representation of knowledge in a particular domain or area of knowledge, such as diseases or anatomy. Terms are arranged in a hierarchy, and the terms and relationships between them are defined using both human readable and machine readable definitions, allowing logical inference and sophisticated queries. They are expressed in a knowledge representation language like RDF or OWL.
What is a phenotype?
A phenotype is an observable characteristic or trait of an organism, like the color of a flower or the shape of an ear.
What is a genotype?
A genotype is a specific variation of a gene that encodes a phenotype, such a gene that encodes eye color or height. Genes are found on chromosomes and encoded in patterns on the DNA molecule. That DNA pattern is copied and used to make proteins by a suite of RNA molecules in the cell. In this way, changes in the DNA pattern that make up our specific genotype affect the proteins that cells make and result in an observable phenotype, like blue eyes or short stature.
How can an ontology help with my research problem?
Ontologies are useful for integrating data that are heterogeneous in scale, terminology, context, and granularity. The inferencing capabilities can aid in hypothesis generation and filling data gaps. The networks of relationships created when ontologies are combined can aid in knowledge discovery and sophisticated queries. Ontologies enable distributed data sets to become truly integrated.
I have information that might help. How can I share it with you?
Please send any information to [email protected].
How can I join your team?
TISLab is always happy to engage with new supporters and collaborators. Feel free to write directly to any TISLab member you would like to engage. If you are looking for employment opportunities, or a student or post-doc position, please see contact us at [email protected] to inquire about open positions.