↓ Skip to main content

Causality in cancer research: a journey through models in molecular epidemiology and their philosophical interpretation

Overview of attention for article published in Emerging Themes in Epidemiology, June 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#49 of 149)
  • Good Attention Score compared to outputs of the same age (74th percentile)

Mentioned by

twitter
11 X users

Citations

dimensions_citation
33 Dimensions

Readers on

mendeley
49 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Causality in cancer research: a journey through models in molecular epidemiology and their philosophical interpretation
Published in
Emerging Themes in Epidemiology, June 2017
DOI 10.1186/s12982-017-0061-7
Pubmed ID
Authors

Paolo Vineis, Phyllis Illari, Federica Russo

Abstract

In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing-notably, the "sufficient-component-cause framework" and the "mark transmission" approach; (b) new acquisitions about disease pathogenesis, e.g. the "branched model" in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics research, with a large number of "signals" and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of "cancer causes". We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called "evidential pluralism". According to this view, causal reasoning is based on both "evidence of difference-making" (e.g. associations) and on "evidence of underlying biological mechanisms". We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social-are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 24%
Researcher 11 22%
Student > Master 6 12%
Professor 3 6%
Student > Doctoral Student 2 4%
Other 5 10%
Unknown 10 20%
Readers by discipline Count As %
Medicine and Dentistry 9 18%
Biochemistry, Genetics and Molecular Biology 4 8%
Agricultural and Biological Sciences 3 6%
Nursing and Health Professions 3 6%
Philosophy 3 6%
Other 15 31%
Unknown 12 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 July 2017.
All research outputs
#4,730,210
of 23,630,563 outputs
Outputs from Emerging Themes in Epidemiology
#49
of 149 outputs
Outputs of similar age
#81,591
of 318,217 outputs
Outputs of similar age from Emerging Themes in Epidemiology
#1
of 2 outputs
Altmetric has tracked 23,630,563 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 149 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 318,217 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them