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Algorithms to Model Single Gene, Single Chromosome, and Whole Genome Copy Number Changes Jointly in Tumor Phylogenetics

Overview of attention for article published in PLoS Computational Biology, July 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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Title
Algorithms to Model Single Gene, Single Chromosome, and Whole Genome Copy Number Changes Jointly in Tumor Phylogenetics
Published in
PLoS Computational Biology, July 2014
DOI 10.1371/journal.pcbi.1003740
Pubmed ID
Authors

Salim Akhter Chowdhury, Stanley E. Shackney, Kerstin Heselmeyer-Haddad, Thomas Ried, Alejandro A. Schäffer, Russell Schwartz

Abstract

We present methods to construct phylogenetic models of tumor progression at the cellular level that include copy number changes at the scale of single genes, entire chromosomes, and the whole genome. The methods are designed for data collected by fluorescence in situ hybridization (FISH), an experimental technique especially well suited to characterizing intratumor heterogeneity using counts of probes to genetic regions frequently gained or lost in tumor development. Here, we develop new provably optimal methods for computing an edit distance between the copy number states of two cells given evolution by copy number changes of single probes, all probes on a chromosome, or all probes in the genome. We then apply this theory to develop a practical heuristic algorithm, implemented in publicly available software, for inferring tumor phylogenies on data from potentially hundreds of single cells by this evolutionary model. We demonstrate and validate the methods on simulated data and published FISH data from cervical cancers and breast cancers. Our computational experiments show that the new model and algorithm lead to more parsimonious trees than prior methods for single-tumor phylogenetics and to improved performance on various classification tasks, such as distinguishing primary tumors from metastases obtained from the same patient population.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Portugal 1 2%
Unknown 47 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 32%
Researcher 12 24%
Student > Master 7 14%
Student > Postgraduate 3 6%
Student > Bachelor 2 4%
Other 3 6%
Unknown 7 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 30%
Computer Science 10 20%
Biochemistry, Genetics and Molecular Biology 8 16%
Medicine and Dentistry 5 10%
Mathematics 1 2%
Other 4 8%
Unknown 7 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 05 September 2017.
All research outputs
#4,760,001
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#3,802
of 8,960 outputs
Outputs of similar age
#43,386
of 239,355 outputs
Outputs of similar age from PLoS Computational Biology
#50
of 163 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 57% 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 239,355 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 81% of its contemporaries.
We're also able to compare this research output to 163 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.