The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Efficient algorithms for inferring evolutionary trees
Abstract
In this paper, we examine two related problems of inferring the evolutionary history of n objects, either from present characters of the objects or from several partial estimates of their evolutionary history. The first problem is called the Phylogeny problem, and second is the Tree Compatibility problem. Both of these problems are central in algorithmic approaches to the study of evolution and in other problems of historical reconstruction. In this paper, we show that both of these problems can be solved by graph theoretic methods in linear time, which is time optimal, and which is a significant improvement over existing methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Booth, K. S., & Lueker, G. S. (1976) · Journal of Computer and System Sciences · doi:10.1016/s0022-0000(76)80045-1 · Cited by 1,488
Cited by
The complexity of reconstructing trees from qualitative characters and subtrees
Steel, M. (1992) · Journal of Classification · doi:10.1007/bf02618470 · Cited by 508
Bounds on the Minimum Number of Recombination Events in a Sample History
Myers, S. R., & Griffiths, R. C. (2003) · Genetics · doi:10.1093/genetics/163.1.375 · Cited by 177
EFFICIENT RECONSTRUCTION OF HAPLOTYPE STRUCTURE VIA PERFECT PHYLOGENY
ESKIN, E., HALPERIN, E., & KARP, R. M. (2003) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720003000174 · Cited by 131
Mapping Trait Loci by Use of Inferred Ancestral Recombination Graphs
Minichiello, M. J., & Durbin, R. (2006) · The American Journal of Human Genetics · doi:10.1086/508901 · Cited by 115
The number of recombination events in a sample history: conflict graph and lower bounds
Bafna, V., & Bansal, V. (2004) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2004.23 · Cited by 39
Metadata
- Authors
- Gusfield, D.
- Venue
- Networks
- Publisher
- Wiley
- Published
- 1991-01
- Volume
- 21
- Issue
- 1
- Pages
- 19-28
- ISSN
- 0028-3045 (ISSN-L)
- 1097-0037
- OpenAlex
- W2074507113
- Cited by
- 399
- References
- 1
- FWCI
- 1.90
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Efficient algorithms for inferring evolutionary trees. https://app.cochat.ai/papers/10.1002/net.3230210104
- The paper
- Gusfield, D. (1991). Efficient algorithms for inferring evolutionary trees. Networks. https://doi.org/10.1002/net.3230210104
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
