Intersection of philosophy and biomedical science
Long linked to the ivory tower, philosophers are having an influence beyond the academy. Take Barry Smith, SUNY Distinguished Professor of Philosophy and director of the National Center for Ontological Research at the University at Buffalo, as an example.
“Most of my work now is in collaboration with people outside of philosophy,” says Smith, who was named one of the 50 most influential living philosophers by TheBestSchools.org. “I sometimes tell people I’m not a philosopher anymore; I’m just an ontologist.”
Ontology is the science of consistently representing massive amounts of data. Ontologies make data more easily searchable and combinable by building common vocabularies whose terms are logically defined and based on rigorous classifications.
Ontological tools and theories are increasingly being applied in bio- and medical informatics, in intelligence, defense and security analysis, in industry, publishing, finance, and other fields, where they serve as a basis for improved classifications, information integration, and automatic reasoning. The iPhone’s Siri app functions on the basis of ontologies that organize the disparate data the program interprets – data about restaurants or movies, for example.
When researchers began decoding the human genome, the immensity of the data sets created an interplay between computer science and biology that hadn’t previously existed. The problem was to find a way of associating sequence data from the human genome or other genomic mapping projects with traditional biological phenomena like cell division, diabetes or visual perception.
“Very quickly people realized that genomic data were going to lend new significance to the results of experiments on what are called model organisms such as mice or flies,” says Smith. “Commonalities in the genomes of different organisms provided a new way to use the results of such experiments for understanding human health and disease. But to bring this about, the same terms would have to be used to describe biological phenomena in model organisms as we use to describe the corresponding phenomena in humans.”
The Gene Ontology (GO) was created in 1998 as the needed species-neutral vocabulary. The GO was then and is still today a tremendously useful tool; but there were, according to Smith, logical problems with the way it was built.
His thought that philosophical ideas from logic and semantics could help biological science is now a commonplace. As he points out, “there is now a Journal of Biomedical Semantics; something that would have been astonishing to former generations of biologists. In fact, many of the things philosophers have been talking about since Aristotle are being rediscovered by information-driven scientists working in areas like biology as they have realized that they need somehow to capture classifications of different kinds of entities, for instance different kinds of cells or different kinds of diseases, in computationally useful ways.”
Smith’s interactions with the Gene Ontology Consortium made him the first philosopher to work with biologists on the problem of logically coherent classification and made him an influential figure in biomedical informatics. Today, Smith serves as an affiliate professor in UB’s Department of Biomedical Informatics and the university’s Division of Biomedical Ontology. The latter, which he helped to found, is the first academic unit in the world with the word “ontology” in its title, he says.
“The Gene Ontology alone has received over $300 million in funding since 1998,” says Smith. “It’s a very important tool for medical research. It’s now used standardly in every drug company and in every medical school as a way of linking biomedical data and genomic data across organisms.”
In addition to his work in biology, Smith is also involved with ontology projects in defense and security; his ontological ideas are used in the United Nations, in the U.S. Geological Survey, the Federal Highways Administration and a series of other bodies.
All of these projects are, he says, designed to solve problems of data that are similar to the types of problems faced by biological and medical researchers. “The same problems arise, over and over, in all areas where data are collected in different ways by different people and need to be unified and reasoned over. Ontology is the job of trying to put data together in coherent ways.”
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