|IDEA BY:||Jonah Librach||LOCATION:||Canada||CATEGORY:||Science/Medical|
|IDEA BY:||Jonah Librach|
A computer programmer discovering biological databases for the first time likely is first enthralled by the reality that they could make true biological discoveries. High throughput biology has clearly revamped biological research, but we are still far from grasping its true potential. The use of experimental ontology in large biological databases is required to reap the time-saving and economic benefits of high throughput biology. Experimental ontology, is a systematic description of experiments and experimental variables. The restricted language in an ontology allows for algorithms to be designed which find similarities between experiments that otherwise might not be noticed if these algorithms relied on natural language processing. For example, biologists could search precisely for similar experiments. A biologist may be unaware of all the proteins with similar functional binding domains to the protein of interest. However, this knowledge is easily stored in a computer which would present this information as a search result. Hopefully, this information would lead to a more specific biological question and saved time and money. Importantly, a proper widespread implementation of personalized medicine would be impossible without widespread use of experimental ontology. If experimental ontology is extended to medical history ontology (formal description of medical history), computers could then construct hypotheses based on medical history and genetics and treatment results because the information had been recorded unambiguously. Currently, large scale meta-analysis is not practiced as commonly as we would like. Promoting the widespread use of experimental ontology would help computational biologists identify experiments which can be used in meta-analyses.