↓ Skip to main content

Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance

Overview of attention for article published in Genome Biology (Online Edition), August 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)

Mentioned by

twitter
18 tweeters
facebook
2 Facebook pages
googleplus
1 Google+ user
f1000
1 research highlight platform

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
52 Mendeley
citeulike
4 CiteULike
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
Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance
Published in
Genome Biology (Online Edition), August 2017
DOI 10.1186/s13059-017-1282-3
Pubmed ID
Authors

Amin Emad, Junmei Cairns, Krishna R. Kalari, Liewei Wang, Saurabh Sinha

Abstract

Identification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance. We developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group. Our results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 21%
Student > Ph. D. Student 11 21%
Student > Master 8 15%
Student > Bachelor 6 12%
Student > Doctoral Student 2 4%
Other 8 15%
Unknown 6 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 27%
Agricultural and Biological Sciences 14 27%
Computer Science 6 12%
Engineering 3 6%
Business, Management and Accounting 1 2%
Other 5 10%
Unknown 9 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 25 December 2017.
All research outputs
#2,035,891
of 19,317,391 outputs
Outputs from Genome Biology (Online Edition)
#1,772
of 3,822 outputs
Outputs of similar age
#43,780
of 284,178 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 19,317,391 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,822 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one has gotten more attention than average, scoring higher than 53% 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 284,178 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 1 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