Synthesizing large-scale species trees using the strict consensus approach
Journal of Bioinformatics and Computational Biology, ISSN: 1757-6334, Vol: 15, Issue: 3, Page: 1740002
2017
- 4Citations
- 2Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations4
- Citation Indexes4
- CrossRef2
- Captures2
- Readers2
Conference Paper Description
Supertree problems are a standard tool for synthesizing large-scale species trees from a given collection of gene trees under some problem-specific objective. Unfortunately, these problems are typically NP-hard, and often remain so when their instances are restricted to rooted gene trees sampled from the same species. While a class of restricted supertree problems has been effectively addressed by the parameterized strict consensus approach, in practice, most gene trees are unrooted and sampled from different species. Here, we overcome this stringent limitation by describing efficient algorithms that are adopting the strict consensus approach to also handle unrestricted supertree problems. Finally, we demonstrate the performance of our algorithms in a comparative study with classic supertree heuristics using simulated and empirical data sets.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85019860173&origin=inward; http://dx.doi.org/10.1142/s0219720017400029; http://www.ncbi.nlm.nih.gov/pubmed/28513253; http://www.worldscientific.com/doi/abs/10.1142/S0219720017400029; http://www.worldscientific.com/doi/pdf/10.1142/S0219720017400029; https://www.worldscientific.com/doi/abs/10.1142/S0219720017400029
World Scientific Pub Co Pte Lt
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