↓ Skip to main content

Revealing cancer subtypes with higher-order correlations applied to imaging and omics data

Overview of attention for article published in BMC Medical Genomics, March 2017
Altmetric Badge

Mentioned by

twitter
2 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
55 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
Revealing cancer subtypes with higher-order correlations applied to imaging and omics data
Published in
BMC Medical Genomics, March 2017
DOI 10.1186/s12920-017-0256-3
Pubmed ID
Authors

Kiley Graim, Tiffany Ting Liu, Achal S. Achrol, Evan O. Paull, Yulia Newton, Steven D. Chang, Griffith R. Harsh, Sergio P. Cordero, Daniel L. Rubin, Joshua M. Stuart

Abstract

Patient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings. Here, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes. In an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification. Subtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 55 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 25%
Researcher 7 13%
Professor > Associate Professor 7 13%
Student > Master 6 11%
Student > Bachelor 4 7%
Other 8 15%
Unknown 9 16%
Readers by discipline Count As %
Computer Science 9 16%
Medicine and Dentistry 8 15%
Agricultural and Biological Sciences 5 9%
Biochemistry, Genetics and Molecular Biology 5 9%
Physics and Astronomy 3 5%
Other 12 22%
Unknown 13 24%

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 28 October 2017.
All research outputs
#17,885,520
of 22,962,258 outputs
Outputs from BMC Medical Genomics
#798
of 1,229 outputs
Outputs of similar age
#220,937
of 309,402 outputs
Outputs of similar age from BMC Medical Genomics
#10
of 14 outputs
Altmetric has tracked 22,962,258 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,229 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 29th percentile – i.e., 29% 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 309,402 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.