Microparadigms: Chains of collective reasoning in publications about molecular interactions
Andrey Rzhetsky * , † , ‡ , § ,
Ivan Iossifov * , † ,
Ji Meng Loh ¶ , and
Author Affiliations
*Department of Biomedical Informatics,
†Columbia Genome Center, and
‡Center for Computational Biology and Bioinformatics, Columbia University, New York, NY 10032;
¶Department of Statistics, Columbia University, New York, NY 10027; and
‖Department of Genetics, Yale University, New Haven, CT 06520
Communicated by Sherman M. Weissman, Yale University School of Medicine, New Haven, CT, January 23, 2006 (received for review August 15, 2005)
Abstract
We analyzed a very large set of molecular interactions that had been derived automatically from biological texts. We found that published statements, regardless of their verity, tend to interfere with interpretation of the subsequent experiments and, therefore, can act as scientific “microparadigms,” similar to dominant scientific theories [Kuhn, T. S. (1996) The Structure of Scientific Revolutions (Univ. Chicago Press, Chicago)]. Using statistical tools, we measured the strength of the influence of a single published statement on subsequent interpretations. We call these measured values the momentums of the published statements and treat separately the majority and minority of conflicting statements about the same molecular event. Our results indicate that, when building biological models based on published experimental data, we may have to treat the data as highly dependent-ordered sequences of statements (i.e., chains of collective reasoning) rather than unordered and independent experimental observations. Furthermore, our computations indicate that our data set can be interpreted in two very different ways (two “alternative universes”): one is an “optimists’ universe” with a very low incidence of false results (<5%), and another is a “pessimists’ universe” with an extraordinarily high rate of false results (>90%). Our computations deem highly unlikely any milder intermediate explanation between these two extremes.
Bayesian inference, quality of science, text mining, experiment interpretation, information cascade
Footnotes
§To whom correspondence should be addressed.
Author contributions: A.R. and K.P.W. designed research; A.R. and I.I. performed research; A.R., I.I., and J.M.L. analyzed data; and A.R. and K.P.W. wrote the paper.
Conflict of interest statement: No conflicts declared.
Freely available online through the PNAS open access option.
© 2006 by The National Academy of Sciences of the USA
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