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An exact algorithm for finding cancer driver somatic genome alterations: the weighted mutually exclusive maximum set cover problem

Overview of attention for article published in Algorithms for Molecular Biology, May 2016
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3 tweeters

Citations

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3 Dimensions

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12 Mendeley
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Title
An exact algorithm for finding cancer driver somatic genome alterations: the weighted mutually exclusive maximum set cover problem
Published in
Algorithms for Molecular Biology, May 2016
DOI 10.1186/s13015-016-0073-9
Pubmed ID
Authors

Songjian Lu, Gunasheil Mandava, Gaibo Yan, Xinghua Lu

Abstract

The mutual exclusivity of somatic genome alterations (SGAs), such as somatic mutations and copy number alterations, is an important observation of tumors and is widely used to search for cancer signaling pathways or SGAs related to tumor development. However, one problem with current methods that use mutual exclusivity is that they are not signal-based; another problem is that they use heuristic algorithms to handle the NP-hard problems, which cannot guarantee to find the optimal solutions of their models. In this study, we propose a novel signal-based method that utilizes the intrinsic relationship between SGAs on signaling pathways and expression changes of downstream genes regulated by pathways to identify cancer signaling pathways using the mutually exclusive property. We also present a relatively efficient exact algorithm that can guarantee to obtain the optimal solution of the new computational model. We have applied our new model and exact algorithm to the breast cancer data. The results reveal that our new approach increases the capability of finding better solutions in the application of cancer research. Our new exact algorithm has a time complexity of [Formula: see text](Note: Following the recent convention, we use a star * to represent that the polynomial part of the time complexity is neglected), which has solved the NP-hard problem of our model efficiently. Our new method and algorithm can discover the true causes behind the phenotypes, such as what SGA events lead to abnormality of the cell cycle or make the cell metastasis lose control in tumors; thus, it identifies the target candidates for precision (or target) therapeutics.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 33%
Other 2 17%
Researcher 2 17%
Student > Ph. D. Student 2 17%
Student > Bachelor 1 8%
Other 1 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 33%
Agricultural and Biological Sciences 2 17%
Engineering 2 17%
Computer Science 1 8%
Neuroscience 1 8%
Other 2 17%

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 18 May 2016.
All research outputs
#8,687,828
of 11,293,566 outputs
Outputs from Algorithms for Molecular Biology
#105
of 177 outputs
Outputs of similar age
#177,012
of 275,701 outputs
Outputs of similar age from Algorithms for Molecular Biology
#9
of 12 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 177 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 35th percentile – i.e., 35% 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 275,701 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.