Conflicting biomedical assumptions for mathematical modeling: the case of cancer metastasis.
PLoS Comput Biol. 2011 Oct; 7(10):e1002132
We believe that we know what others have claimed to have discovered, and that any important developments affecting our research area will eventually become known to us. In an elegant and original manner, Divoli et al. demonstrate that those beliefs are not supported by the evidence.
Over a million new research articles are catalogued every year by Web of Science. A non-negligible fraction of those articles would be of potential relevance to the research interest of many life-sciences scientists. Clearly we are struggling with a Library of Babel-like problem: too many potential sources where the nuggets we are looking for could be hidden.
I certainly have read many an article that cites my work in the wrong context or even uses my work to support the opposite claim from the one I made. Divoli et al. demonstrate the extent of this problem. Their work supports the need to revamp the manner in which we publish our research.
Amaral L: F1000Prime Recommendation of [Divoli A et al., PLoS Comput Biol 2011, 7(10):e1002132]. In F1000Prime, 09 Dec 2011; DOI: 10.3410/f.13383005.14750246. F1000Prime.com/13383005#eval14750246
F1000Prime Recommendations, Dissents and Comments for [Divoli A et al., PLoS Comput Biol 2011, 7(10):e1002132]. In F1000Prime, 12 Dec 2013; F1000Prime.com/13383005
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Nat Med. 2012 May 20
Computational models in biomedicine rely on biological and clinical assumptions. The selection of these assumptions contributes substantially to modeling success or failure. Assumptions used by experts at the cutting edge of research, however, are rarely explicitly described in scientific publications. One can directly collect and assess some of these assumptions through interviews and surveys. Here we investigate diversity in expert views about a complex biological phenomenon, the process of cancer metastasis. We harvested individual viewpoints from 28 experts in clinical and molecular aspects of cancer metastasis and summarized them computationally. While experts predominantly agreed on the definition of individual steps involved in metastasis, no two expert scenarios for metastasis were identical. We computed the probability that any two experts would disagree on k or fewer metastatic stages and found that any two randomly selected experts are likely to disagree about several assumptions. Considering the probability that two or more of these experts review an article or a proposal about metastatic cascades, the probability that they will disagree with elements of a proposed model approaches 1. This diversity of conceptions has clear consequences for advance and deadlock in the field. We suggest that strong, incompatible views are common in biomedicine but largely invisible to biomedical experts themselves. We built a formal Markov model of metastasis to encapsulate expert convergence and divergence regarding the entire sequence of metastatic stages. This model revealed stages of greatest disagreement, including the points at which cancer enters and leaves the bloodstream. The model provides a formal probabilistic hypothesis against which researchers can evaluate data on the process of metastasis. This would enable subsequent improvement of the model through Bayesian probabilistic update. Practically, we propose that model assumptions and hunches be harvested systematically and made available for modelers and scientists.
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