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Linking Physical Activity to Breast Cancer: Text Mining Results and a Protocol for Systematically Reviewing Three Potential Mechanistic Pathways.

ABSTRACT: Epidemiologic research suggests that physical activity is associated with a reduced risk of breast cancer, but the causal nature of this link is not clear. Investigating mechanistic pathways can provide evidence of biological plausibility and improve causal inference. This project will examine three putative pathways (sex steroid hormones, insulin signaling, and inflammation) in a series of two-stage systematic reviews. Stage 1 used Text Mining for Mechanism Prioritisation (TeMMPo) to identify and prioritize relevant biological intermediates. Stage 2 will systematically review the findings from studies of (i) physical activity and intermediates and (ii) intermediates and breast cancer. Ovid MEDLINE, EMBASE, and SPORTDiscus will be searched using a combination of subject headings and free-text terms. Human intervention and prospective, observational studies will be eligible for inclusion. Meta-analysis will be performed where possible. Risk of bias will be assessed using the Cochrane Collaboration tool, or the ROBINS-I or ROBINS-E tool, depending on study type. Strength of evidence will be assessed using the GRADE system. In addition to synthesizing the mechanistic evidence that links physical activity with breast cancer risk, this project may also identify priority areas for future research and help inform the design and implementation of physical activity interventions.See related reviews by Swain et al., p. 16 and Drummond et al., p. 28.

SUBMITTER: Lynch BM

PROVIDER: S-EPMC8755571 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate.

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Linking Physical Activity to Breast Cancer: Text Mining Results and a Protocol for Systematically Reviewing Three Potential Mechanistic Pathways.

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