Literature DB >> 15746275

Partition-distance via the assignment problem.

Dmitry A Konovalov1, Bruce Litow, Nigel Bajema.   

Abstract

MOTIVATION: Accuracy testing of various pedigree reconstruction methods requires an efficient algorithm for the calculation of distance between a known partition and its reconstruction. The currently used algorithm of Almudevar and Field takes a prohibitively long time for certain partitions and population sizes.
RESULTS: We present an algorithm that very efficiently reduces the partition-distance calculation to the classic assignment problem of weighted bipartite graphs that has known polynomial-time solutions. The performance of the algorithm is tested against the Almudevar and Field partition-distance algorithm to verify the significant improvement in speed. AVAILABILITY: Computer code written in java is available upon request from the first author.

Mesh:

Year:  2005        PMID: 15746275     DOI: 10.1093/bioinformatics/bti373

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  6 in total

1.  Joint inference of population assignment and demographic history.

Authors:  Sang Chul Choi; Jody Hey
Journal:  Genetics       Date:  2011-07-20       Impact factor: 4.562

2.  Comparing Phylogenetic Trees by Matching Nodes Using the Transfer Distance Between Partitions.

Authors:  Damian Bogdanowicz; Krzysztof Giaro
Journal:  J Comput Biol       Date:  2017-02-08       Impact factor: 1.479

3.  Characterization of a Bayesian genetic clustering algorithm based on a Dirichlet process prior and comparison among Bayesian clustering methods.

Authors:  Akio Onogi; Masanobu Nurimoto; Mitsuo Morita
Journal:  BMC Bioinformatics       Date:  2011-06-28       Impact factor: 3.169

4.  Finding the mean in a partition distribution.

Authors:  Thomas J Glassen; Timo von Oertzen; Dmitry A Konovalov
Journal:  BMC Bioinformatics       Date:  2018-10-12       Impact factor: 3.169

5.  Using the Dirichlet process to form clusters of people's concerns in the context of future party identification.

Authors:  Patrick Meyer; Fenja M Schophaus; Thomas Glassen; Jasmin Riedl; Julia M Rohrer; Gert G Wagner; Timo von Oertzen
Journal:  PLoS One       Date:  2019-03-04       Impact factor: 3.240

6.  PCA-based population structure inference with generic clustering algorithms.

Authors:  Chih Lee; Ali Abdool; Chun-Hsi Huang
Journal:  BMC Bioinformatics       Date:  2009-01-30       Impact factor: 3.169

  6 in total

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