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{bio,medical} informatics

Friday, March 29, 2002

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find related articles. powered by google. Stanford Medical Informatics Preprint Archive Associating Genes with Gene Ontology Codes Using a Maximum Entropy Analysis of Biomedical Literature

"Functional characterizations of thousands of gene products from many species are described in the published literature. These discussions are extremely valuable for characterizing the functions not only of these gene products, but also of their homologs in other organisms. The Gene Ontology (GO) is an effort to create a controlled terminology for labeling gene functions in a more precise, reliable, computer-readable manner. Currently, the best annotations of gene function with the GO are performed by highly trained biologists who read the literature and select appropriate codes. In this study, we explored the possibility that statistical natural language processing techniques can be used to assign GO codes. We compared three document classification methods (maximum entropy modeling, naïve Bayes classification, and nearest-neighbor classification) to the problem of associating a set of GO codes (for biological process) to literature abstracts and thus to the genes associated with the abstracts. We showed that maximum entropy modeling outperforms the other methods and achieves an accuracy of 72% when ascertaining the function discussed within an abstract. The maximum entropy method provides confidence measures that correlate well with performance. We conclude that statistical methods may be used to assign GO codes and may be useful for the difficult task of reassignment as terminology standards evolve over time."

redux [03.17.02]
find related articles. powered by google. The Scientist Life Sentences
[requires 'free' registration]

"The great challenge in biological research today is how to turn data into knowledge. I have met people who think data is knowledge but these people are then striving for a means of turning knowledge into understanding. Knowledge and science are related words and to know, I believe, is to understand. Before rushing to convert genomics to 'genamics' and finding that it is another dead end, we should consider evacuating the Tower of Babel. We need a theoretical framework in which to embed biological data so that the endless stream of data, filled with the flotsam and jetsam of evolution, can be sifted and abstracted.

Very simply, the network we should be interested is not the network of names but the network of the objects themselves. The language of these objects is not the Oxford Dictionary of Molecular Biology—the Ontology Consortium's main source—but that of molecular recognition, the language of molecular biology itself."

[ rhetoric ]

Bioinformatics will be at the core of biology in the 21st century. In fields ranging from structural biology to genomics to biomedical imaging, ready access to data and analytical tools are fundamentally changing the way investigators in the life sciences conduct research and approach problems. Complex, computationally intensive biological problems are now being addressed and promise to significantly advance our understanding of biology and medicine. No biological discipline will be unaffected by these technological breakthroughs.


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