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Molecular pathological epidemiology: new developing frontiers of big data science to study etiologies and pathogenesis

Overview of attention for article published in Journal of Gastroenterology, October 2016
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Title
Molecular pathological epidemiology: new developing frontiers of big data science to study etiologies and pathogenesis
Published in
Journal of Gastroenterology, October 2016
DOI 10.1007/s00535-016-1272-3
Pubmed ID
Authors

Tsuyoshi Hamada, NaNa Keum, Reiko Nishihara, Shuji Ogino

Abstract

Molecular pathological epidemiology (MPE) is an integrative field that utilizes molecular pathology to incorporate interpersonal heterogeneity of a disease process into epidemiology. In each individual, the development and progression of a disease are determined by a unique combination of exogenous and endogenous factors, resulting in different molecular and pathological subtypes of the disease. Based on "the unique disease principle," the primary aim of MPE is to uncover an interactive relationship between a specific environmental exposure and disease subtypes in determining disease incidence and mortality. This MPE approach can provide etiologic and pathogenic insights, potentially contributing to precision medicine for personalized prevention and treatment. Although breast, prostate, lung, and colorectal cancers have been among the most commonly studied diseases, the MPE approach can be used to study any disease. In addition to molecular features, host immune status and microbiome profile likely affect a disease process, and thus serve as informative biomarkers. As such, further integration of several disciplines into MPE has been achieved (e.g., pharmaco-MPE, immuno-MPE, and microbial MPE), to provide novel insights into underlying etiologic mechanisms. With the advent of high-throughput sequencing technologies, available genomic and epigenomic data have expanded dramatically. The MPE approach can also provide a specific risk estimate for each disease subgroup, thereby enhancing the impact of genome-wide association studies on public health. In this article, we present recent progress of MPE, and discuss the importance of accounting for the disease heterogeneity in the era of big-data health science and precision medicine.

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The data shown below were collected from the profiles of 2 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 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 125 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 22%
Student > Ph. D. Student 14 11%
Student > Master 10 8%
Lecturer 7 6%
Student > Doctoral Student 7 6%
Other 31 25%
Unknown 29 23%
Readers by discipline Count As %
Medicine and Dentistry 29 23%
Biochemistry, Genetics and Molecular Biology 16 13%
Agricultural and Biological Sciences 9 7%
Business, Management and Accounting 5 4%
Computer Science 5 4%
Other 25 20%
Unknown 36 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 07 September 2017.
All research outputs
#15,392,529
of 22,899,952 outputs
Outputs from Journal of Gastroenterology
#740
of 1,093 outputs
Outputs of similar age
#201,275
of 319,493 outputs
Outputs of similar age from Journal of Gastroenterology
#11
of 18 outputs
Altmetric has tracked 22,899,952 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,093 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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 319,493 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.