Title |
Robustness of birth-death and gain models for inferring evolutionary events
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Published in |
BMC Genomics, October 2014
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DOI | 10.1186/1471-2164-15-s6-s9 |
Pubmed ID | |
Authors |
Maureen Stolzer, Larry Wasserman, Dannie Durand |
Abstract |
Phylogenetic birth-death models are opening a new window on the processes of genome evolution in studies of the evolution of gene and protein families, protein-protein interaction networks, microRNAs, and copy number variation. Given a species tree and a set of genomic characters in present-day species, the birth-death approach estimates the most likely rates required to explain the observed data and returns the expected ancestral character states and the history of character state changes. Achieving a balance between model complexity and generalizability is a fundamental challenge in the application of birth-death models. While more parameters promise greater accuracy and more biologically realistic models, increasing model complexity can lead to overfitting and a heavy computational cost. |
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Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 30 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 12 | 40% |
Researcher | 8 | 27% |
Student > Master | 3 | 10% |
Student > Doctoral Student | 2 | 7% |
Professor > Associate Professor | 1 | 3% |
Other | 1 | 3% |
Unknown | 3 | 10% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 14 | 47% |
Biochemistry, Genetics and Molecular Biology | 6 | 20% |
Computer Science | 4 | 13% |
Physics and Astronomy | 1 | 3% |
Medicine and Dentistry | 1 | 3% |
Other | 0 | 0% |
Unknown | 4 | 13% |