Bioinformatics Vol. 18 no. 90001 2002
Pages S249-S257
© 2002 Oxford University Press
Of truth and pathways: chasing bits of information through myriads of articles
1 Department of Medical Informatics, Columbia University,
New York, NY 10032, USA
2 Columbia Genome Center, Columbia University,
New York, NY 10032, USA
3 Department of Computer Science, Queens College CUNY,
Flushing, NY 11367, USA
4 Department of Computer Science, Columbia University,
New York, NY 10027, USA
Received on January 24, 2002
; revised on April 1, 2002
; accepted on April 1, 2002
Motivation: Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarly hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.
Contact: ar345{at}columbia.edu
Keywords: statistical modelling; scientometric analysis; molecular interaction data; natural language processing
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