Microparadigms: Chains of collective reasoning in publications about molecular interactions

Andrey Rzhetsky, Ivan Iossifov, Ji Meng Loh, Kevin P. White

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)4940-4945
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number13
StatePublished - Mar 28 2006
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General


  • Bayesian inference
  • Experiment interpretation
  • Information cascade
  • Quality of science
  • Text mining


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