Who is Who in Phylogenetic Networks

Publications of Juan Wang     Order by:   Type | Year

Associated keywords

distance-between-networks evaluation explicit-network from-clusters from-network from-rooted-trees heuristic kth-order-reduced-network mu-distance partly-reduced-networks phylogenetic-network phylogeny polynomial Program-BIMLR Program-Dendroscope Program-Frin Program-IGNet Program-LNetwork Program-QuickCass reconstruction reduced-networks software survey tree-sibling-network tree-child-network

2019

1 Juan Wang and Maozu Guo. IGNet: Constructing Rooted Phylogenetic Networks Based on Incompatible Graphs. In ICNC-FSKD19, Vol. 1075:894-900 of Advances in Intelligent Systems and Computing, Springer, 2019. Keywords: explicit network, from rooted trees, phylogenetic network, phylogeny, Program BIMLR, Program IGNet, Program LNetwork, reconstruction, software.         2 Yan Hong and Juan Wang. Frin: An Efficient Method for Representing Genome Evolutionary History. In Frontiers in Genetics, Vol. 10(1261):1-8, 2019. Keywords: explicit network, from clusters, phylogenetic network, phylogeny, Program Frin, reconstruction. Note: https://doi.org/10.3389/fgene.2019.01261.         3 Juan Wang and Maozu Guo. A review of metrics measuring dissimilarity for rooted phylogenetic networks. In Briefings in Bioinformatics, Vol. 20(6):1972-1980, 2019. Keywords: distance between networks, explicit network, from network, mu distance, phylogenetic network, phylogeny, survey, tree sibling network, tree-child network.

2017

4 Juan Wang and Maozu Guo. A Metric on the Space of kth-order reduced Phylogenetic Networks. In Scientific Reports, Vol. 7(3189):1-10, 2017. Keywords: distance between networks, explicit network, from network, kth-order reduced network, phylogenetic network, phylogeny, polynomial. Note: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5466651/.

2016

5 Juan Wang. A Metric on the Space of Partly Reduced Phylogenetic Networks. In BMRI, Vol. 2016(7534258):1-9, 2016. Keywords: distance between networks, partly reduced networks, phylogenetic network, phylogeny, polynomial, reduced networks. Note: http://downloads.hindawi.com/journals/bmri/aip/7534258.pdf.         6 Juan Wang, Zhang Zhibin and Yanjuan Li. Constructing phylogenetic networks based on the isomorphism of datasets. In BMRI, Vol. 2016(4236858):1-7, 2016. Keywords: from clusters, phylogenetic network, phylogeny, reconstruction. Note: http://downloads.hindawi.com/journals/bmri/aip/4236858.pdf.         7 Juan Wang. A Survey of Methods for Constructing Rooted Phylogenetic Networks. In PLoS ONE, Vol. 11(11):e0165834, 2016. Keywords: evaluation, explicit network, from clusters, phylogenetic network, phylogeny, Program BIMLR, Program Dendroscope, Program LNetwork, reconstruction, survey. Note: http://dx.doi.org/10.1371/journal.pone.0165834.

2014

8 Juan Wang. A new algorithm to construct phylogenetic networks from trees. In Genetics and Molecular Research, Vol. 13(1):1456-1464, 2014. Keywords: explicit network, from clusters, heuristic, phylogenetic network, Program LNetwork, Program QuickCass, reconstruction. Note: http://dx.doi.org/10.4238/2014.March.6.4.         Toggle abstract "Developing appropriate methods for constructing phylogenetic networks from tree sets is an important problem, and much research is currently being undertaken in this area. BIMLR is an algorithm that constructs phylogenetic networks from tree sets. The algorithm can construct a much simpler network than other available methods. Here, we introduce an improved version of the BIMLR algorithm, QuickCass. QuickCass changes the selection strategy of the labels of leaves below the reticulate nodes, i.e., the nodes with an indegree of at least 2 in BIMLR. We show that QuickCass can construct simpler phylogenetic networks than BIMLR. Furthermore, we show that QuickCass is a polynomial-time algorithm when the output network that is constructed by QuickCass is binary. © FUNPEC-RP."

2013

9 Juan Wang, Maozu Guo, Xiaoyan Liu, Yang Liu, Chunyu Wang, Linlin Xing and Kai Che. LNETWORK: An Efficient and Effective Method for Constructing Phylogenetic Networks. In BIO, Vol. 29(18):2269-2276, 2013. Keywords: explicit network, from rooted trees, phylogenetic network, phylogeny, Program LNetwork, reconstruction, software.         Toggle abstract "Motivation: The evolutionary history of species is traditionally represented with a rooted phylogenetic tree. Each tree comprises a set of clusters, i.e. subsets of the species that are descended from a common ancestor. When rooted phylogenetic trees are built from several different datasets (e.g. from different genes), the clusters are often conflicting. These conflicting clusters cannot be expressed as a simple phylogenetic tree; however, they can be expressed in a phylogenetic network. Phylogenetic networks are a generalization of phylogenetic trees that can account for processes such as hybridization, horizontal gene transfer and recombination, which are difficult to represent in standard tree-like models of evolutionary histories. There is currently a large body of research aimed at developing appropriate methods for constructing phylogenetic networks from cluster sets. The Cass algorithm can construct a much simpler network than other available methods, but is extremely slow for large datasets or for datasets that need lots of reticulate nodes. The networks constructed by Cass are also greatly dependent on the order of input data, i.e. it generally derives different phylogenetic networks for the same dataset when different input orders are used.Results: In this study, we introduce an improved Cass algorithm, Lnetwork, which can construct a phylogenetic network for a given set of clusters. We show that Lnetwork is significantly faster than Cass and effectively weakens the influence of input data order. Moreover, we show that Lnetwork can construct a much simpler network than most of the other available methods. © The Author 2013." 10 Juan Wang, Maozu Guo, Linlin Xing, Kai Che, Xiaoyan Liu and Chunyu Wang. BIMLR: A Method for Constructing Rooted Phylogenetic Networks from Rooted Phylogenetic Trees. In Gene, Vol. 527(1):344-351, 2013. Keywords: explicit network, from clusters, from rooted trees, phylogenetic network, phylogeny, Program BIMLR, Program Dendroscope, reconstruction, software.         Toggle abstract "Rooted phylogenetic trees constructed from different datasets (e.g. from different genes) are often conflicting with one another, i.e. they cannot be integrated into a single phylogenetic tree. Phylogenetic networks have become an important tool in molecular evolution, and rooted phylogenetic networks are able to represent conflicting rooted phylogenetic trees. Hence, the development of appropriate methods to compute rooted phylogenetic networks from rooted phylogenetic trees has attracted considerable research interest of late. The CASS algorithm proposed by van Iersel et al. is able to construct much simpler networks than other available methods, but it is extremely slow, and the networks it constructs are dependent on the order of the input data. Here, we introduce an improved CASS algorithm, BIMLR. We show that BIMLR is faster than CASS and less dependent on the input data order. Moreover, BIMLR is able to construct much simpler networks than almost all other methods. BIMLR is available at http://nclab.hit.edu.cn/wangjuan/BIMLR/. © 2013 Elsevier B.V."