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A mixed integer linear programming model to reconstruct phylogenies from single nucleotide polymorphism haplotypes under the maximum parsimony criterion

Overview of attention for article published in Algorithms for Molecular Biology, January 2013
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Title
A mixed integer linear programming model to reconstruct phylogenies from single nucleotide polymorphism haplotypes under the maximum parsimony criterion
Published in
Algorithms for Molecular Biology, January 2013
DOI 10.1186/1748-7188-8-3
Pubmed ID
Authors

Daniele Catanzaro, Ramamoorthi Ravi, Russell Schwartz

Abstract

Phylogeny estimation from aligned haplotype sequences has attracted more and more attention in the recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from medical research, to drug discovery, to epidemiology, to population dynamics. The literature on molecular phylogenetics proposes a number of criteria for selecting a phylogeny from among plausible alternatives. Usually, such criteria can be expressed by means of objective functions, and the phylogenies that optimize them are referred to as optimal. One of the most important estimation criteria is the parsimony which states that the optimal phylogeny T∗for a set H of n haplotype sequences over a common set of variable loci is the one that satisfies the following requirements: (i) it has the shortest length and (ii) it is such that, for each pair of distinct haplotypes hi,hj∈H, the sum of the edge weights belonging to the path from hi to hj in T∗ is not smaller than the observed number of changes between hi and hj. Finding the most parsimonious phylogeny for H involves solving an optimization problem, called the Most Parsimonious Phylogeny Estimation Problem (MPPEP), which is NP-hard in many of its versions.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 39%
Student > Ph. D. Student 5 28%
Other 2 11%
Professor > Associate Professor 2 11%
Student > Master 1 6%
Other 0 0%
Unknown 1 6%
Readers by discipline Count As %
Computer Science 8 44%
Agricultural and Biological Sciences 5 28%
Business, Management and Accounting 2 11%
Mathematics 2 11%
Unknown 1 6%

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 03 February 2013.
All research outputs
#11,053,396
of 12,434,754 outputs
Outputs from Algorithms for Molecular Biology
#148
of 181 outputs
Outputs of similar age
#215,629
of 256,024 outputs
Outputs of similar age from Algorithms for Molecular Biology
#8
of 8 outputs
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So far Altmetric has tracked 181 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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