Journal articles: 'Evolutionary tree'

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Kao, Ming-Yang. "Tree Contractions and Evolutionary Trees." SIAM Journal on Computing 27, no. 6 (December 1998): 1592–616. http://dx.doi.org/10.1137/s0097539795283504.

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Sainudiin, Raazesh, and Amandine Véber. "A Beta-splitting model for evolutionary trees." Royal Society Open Science 3, no. 5 (May 2016): 160016. http://dx.doi.org/10.1098/rsos.160016.

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In this article, we construct a generalization of the Blum–François Beta-splitting model for evolutionary trees, which was itself inspired by Aldous' Beta-splitting model on cladograms. The novelty of our approach allows for asymmetric shares of diversification rates (or diversification ‘potential’) between two sister species in an evolutionarily interpretable manner, as well as the addition of extinction to the model in a natural way. We describe the incremental evolutionary construction of a tree with n leaves by splitting or freezing extant lineages through the generating, organizing and deleting processes. We then give the probability of any (binary rooted) tree under this model with no extinction, at several resolutions: ranked planar trees giving asymmetric roles to the first and second offspring species of a given species and keeping track of the order of the speciation events occurring during the creation of the tree, unranked planar trees , ranked non-planar trees and finally ( unranked non-planar ) trees . We also describe a continuous-time equivalent of the generating, organizing and deleting processes where tree topology and branch lengths are jointly modelled and provide code in SageMath/Python for these algorithms.

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Iannone III, Basil V., Kevin M. Potter, Qinfeng Guo, Insu Jo, Christopher M. Oswalt, and Songlin Fei. "Environmental harshness drives spatial heterogeneity in biotic resistance." NeoBiota 40 (December 4, 2018): 87–105. http://dx.doi.org/10.3897/neobiota.40.28558.

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Ecological communities often exhibit greater resistance to biological invasions when these communities consist of species that are not closely related. The effective size of this resistance, however, varies geographically. Here we investigate the drivers of this heterogeneity in the context of known contributions of native trees to the resistance of forests in the eastern United States of America to plant invasions. Using 42,626 spatially referenced forest community observations, we quantified spatial heterogeneity in relationships between evolutionary relatedness amongst native trees and both invasive plant species richness and cover. We then modelled the variability amongst the 91 ecological sections of our study area in the slopes of these relationships in response to three factors known to affect invasion and evolutionary relationships –environmental harshness (as estimated via tree height), relative tree density and environmental variability. Invasive species richness and cover declined in plots having less evolutionarily related native trees. The degree to which they did, however, varied considerably amongst ecological sections. This variability was explained by an ecological section’s mean maximum tree height and, to a lesser degree, SD in maximum tree height (R2GLMM = 0.47 to 0.63). In general, less evolutionarily related native tree communities better resisted overall plant invasions in less harsh forests and in forests where the degree of harshness was more homogenous. These findings can guide future investigations aimed at identifying the mechanisms by which evolutionary relatedness of native species affects exotic species invasions and the environmental conditions under which these effects are most pronounced.

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Kim, Jaehee, Noah A. Rosenberg, and Julia A. Palacios. "Distance metrics for ranked evolutionary trees." Proceedings of the National Academy of Sciences 117, no. 46 (November 2, 2020): 28876–86. http://dx.doi.org/10.1073/pnas.1922851117.

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Genealogical tree modeling is essential for estimating evolutionary parameters in population genetics and phylogenetics. Recent mathematical results concerning ranked genealogies without leaf labels unlock opportunities in the analysis of evolutionary trees. In particular, comparisons between ranked genealogies facilitate the study of evolutionary processes of different organisms sampled at multiple time periods. We propose metrics on ranked tree shapes and ranked genealogies for lineages isochronously and heterochronously sampled. Our proposed tree metrics make it possible to conduct statistical analyses of ranked tree shapes and timed ranked tree shapes or ranked genealogies. Such analyses allow us to assess differences in tree distributions, quantify estimation uncertainty, and summarize tree distributions. We show the utility of our metrics via simulations and an application in infectious diseases.

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Brunello, Andrea, Enrico Marzano, Angelo Montanari, and Guido Sciavicco. "Decision Tree Pruning via Multi-Objective Evolutionary Computation." International Journal of Machine Learning and Computing 7, no. 6 (December 2017): 167–75. http://dx.doi.org/10.18178/ijmlc.2017.7.6.641.

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Coelho de Souza, Fernanda, Kyle G. Dexter, Oliver L. Phillips, Roel J. W. Brienen, Jerome Chave, David R. Galbraith, Gabriela Lopez Gonzalez, et al. "Evolutionary heritage influences Amazon tree ecology." Proceedings of the Royal Society B: Biological Sciences 283, no. 1844 (December 14, 2016): 20161587. http://dx.doi.org/10.1098/rspb.2016.1587.

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Lineages tend to retain ecological characteristics of their ancestors through time. However, for some traits, selection during evolutionary history may have also played a role in determining trait values. To address the relative importance of these processes requires large-scale quantification of traits and evolutionary relationships among species. The Amazonian tree flora comprises a high diversity of angiosperm lineages and species with widely differing life-history characteristics, providing an excellent system to investigate the combined influences of evolutionary heritage and selection in determining trait variation. We used trait data related to the major axes of life-history variation among tropical trees (e.g. growth and mortality rates) from 577 inventory plots in closed-canopy forest, mapped onto a phylogenetic hypothesis spanning more than 300 genera including all major angiosperm clades to test for evolutionary constraints on traits. We found significant phylogenetic signal (PS) for all traits, consistent with evolutionarily related genera having more similar characteristics than expected by chance. Although there is also evidence for repeated evolution of pioneer and shade tolerant life-history strategies within independent lineages, the existence of significant PS allows clearer predictions of the links between evolutionary diversity, ecosystem function and the response of tropical forests to global change.

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Andrews, Peter. "Climbing the evolutionary tree." Nature 435, no. 7038 (May 2005): 24–25. http://dx.doi.org/10.1038/435024a.

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Nikkhah, Vageehe, Seyed M. Babamir, and Seyed S. Arab. "Estimating Bifurcating Consensus Phylogenetic Trees Using Evolutionary Imperialist Competitive Algorithm." Current Bioinformatics 14, no. 8 (December 13, 2019): 728–39. http://dx.doi.org/10.2174/1574893614666190225145620.

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Background:One of the important goals of phylogenetic studies is the estimation of species-level phylogeny. A phylogenetic tree is an evolutionary classification of different species of creatures. There are several methods to generate such trees, where each method may produce a number of different trees for the species. By choosing the same proteins of all species, it is possible that the topology and arrangement of trees would be different.Objective:There are methods by which biologists summarize different phylogenetic trees to a tree, called consensus tree. A consensus method deals with the combination of gene trees to estimate a species tree. As the phylogenetic trees grow and their number is increased, estimating a consensus tree based on the species-level phylogenetic trees becomes a challenge.Methods:The current study aims at using the Imperialist Competitive Algorithm (ICA) to estimate bifurcating consensus trees. Evolutionary algorithms like ICA are suitable to resolve problems with the large space of candidate solutions.Results:The obtained consensus tree has more similarity to the native phylogenetic tree than related studies.Conclusion:The proposed method enjoys mechanisms and policies that enable us more than other evolutionary algorithms in tuning the proposed algorithm. Thanks to these policies and the mechanisms, the algorithm enjoyed efficiently in obtaining the optimum consensus tree. The algorithm increased the possibility of selecting an optimum solution by imposing some changes in its parameters.

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Hellström, Nils Petter. "Darwin and the Tree of Life: the roots of the evolutionary tree." Archives of Natural History 39, no. 2 (October 2012): 234–52. http://dx.doi.org/10.3366/anh.2012.0092.

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To speak of evolutionary trees and of the Tree of Life has become routine in evolution studies, despite recurrent objections. Because it is not immediately obvious why a tree is suited to represent evolutionary history – woodland trees do not have their buds in the present and their trunks in the past, for a start – the reason why trees make sense to us is historically and culturally, not scientifically, predicated. To account for the Tree of Life, simultaneously genealogical and cosmological, we must explore the particular context in which Darwin declared the natural order to be analogous to a pedigree, and in which he communicated this vision by recourse to a tree. The name he gave his tree reveals part of the story, as before Darwin's appropriation of it, the Tree of Life grew in Paradise at the heart of God's creation.

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DiNardo, Zach, Kiran Tomlinson, Anna Ritz, and Layla Oesper. "Distance measures for tumor evolutionary trees." Bioinformatics 36, no. 7 (November 21, 2019): 2090–97. http://dx.doi.org/10.1093/bioinformatics/btz869.

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Abstract Motivation There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology. Results Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. Availability and implementation Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist. Supplementary information Supplementary data are available at Bioinformatics online.

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Juan, Justie Su-Tzu, Yi-Ching Chen, Chen-Hui Lin, and Shu-Chuan Chen. "Efficient Approaches to the Mixture Distance Problem." Algorithms 13, no. 12 (November 28, 2020): 314. http://dx.doi.org/10.3390/a13120314.

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The ancestral mixture model, an important model building a hierarchical tree from high dimensional binary sequences, was proposed by Chen and Lindsay in 2006. As a phylogenetic tree (or evolutionary tree), a mixture tree created from ancestral mixture models, involves the inferred evolutionary relationships among various biological species. Moreover, it contains the information of time when the species mutates. The tree comparison metric, an essential issue in bioinformatics, is used to measure the similarity between trees. To our knowledge, however, the approach to the comparison between two mixture trees is still unknown. In this paper, we propose a new metric named the mixture distance metric, to measure the similarity of two mixture trees. It uniquely considers the factor of evolutionary times between trees. If we convert the mixture tree that contains the information of mutation time of each internal node into a weighted tree, the mixture distance metric is very close to the weighted path difference distance metric. Since the converted mixture tree forms a special weighted tree, we were able to design a more efficient algorithm to calculate this new metric. Therefore, we developed two algorithms to compute the mixture distance between two mixture trees. One requires O(n2) and the other requires O(nh1h2) computational time with O(n) preprocessing time, where n denotes the number of leaves in the two mixture trees, and h1 and h2 denote the heights of these two trees.

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Rusin, L. Y., E. V. Lyubetskaya, K. Y. Gorbunov, and V. A. Lyubetsky. "Reconciliation of Gene and Species Trees." BioMed Research International 2014 (2014): 1–22. http://dx.doi.org/10.1155/2014/642089.

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The first part of the paper briefly overviews the problem of gene and species trees reconciliation with the focus on defining and algorithmic construction of the evolutionary scenario. Basic ideas are discussed for the aspects of mapping definitions, costs of the mapping and evolutionary scenario, imposing time scales on a scenario, incorporating horizontal gene transfers, binarization and reconciliation of polytomous trees, and construction of species trees and scenarios. The review does not intend to cover the vast diversity of literature published on these subjects. Instead, the authors strived to overview the problem of the evolutionary scenario as a central concept in many areas of evolutionary research. The second part provides detailed mathematical proofs for the solutions of two problems: (i) inferring a gene evolution along a species tree accounting for various types of evolutionary events and (ii) trees reconciliation into a single species tree when only gene duplications and losses are allowed. All proposed algorithms have a cubic time complexity and are mathematically proved to find exact solutions. Solving algorithms for problem (ii) can be naturally extended to incorporate horizontal transfers, other evolutionary events, and time scales on the species tree.

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Dediu, Adrian Horia. "Applications of Evolutionary Algorithms in Formal Languages." Triangle, no. 6 (June 28, 2018): 29. http://dx.doi.org/10.17345/triangle6.29-66.

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Starting from the model proposed by means of Grammatical Evolution, we extend the applicability of the parallel and cooperative searching processes of Evolutionary Algorithms to a new topic: Tree Adjoining Grammar parsing. We evolved derived trees using a string-tree-representation.We also used a linear matching function to compare the yield of a derived tree with a given input. The running tests presented several encouraging results. A post running analysis allowed us to propose several research directions for extending the currently known computational mechanisms in the mildly context sensitive class of languages.

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Kumar, Sunil, Saroj Ratnoo, and Jyoti Vashishtha. "HYPER HEURISTIC EVOLUTIONARY APPROACH FOR CONSTRUCTING DECISION TREE CLASSIFIERS." Journal of Information and Communication Technology 20, Number 2 (February 21, 2021): 249–76. http://dx.doi.org/10.32890/jict2021.20.2.5.

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Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight. Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. While making an effective decision tree classifier with high accuracy and comprehensibility, we need to address the question of setting optimal values for its hyper parameters like the maximum size of the tree, the minimum number of instances required in a node for inducing a split, node splitting criterion and the amount of pruning. The hyper parameter setting influences the performance of the decision tree model. As researchers, we know that no single setting of hyper parameters works equally well for different datasets. A particular setting that gives an optimal decision tree for one dataset may produce a sub-optimal decision tree model for another dataset. In this paper, we present a hyper heuristic approach for tuning the hyper parameters of Recursive and Partition Trees (rpart), which is a typical implementation of CART in statistical and data analytics package R. We employ an evolutionary algorithm as hyper heuristic for tuning the hyper parameters of the decision tree classifier. The approach is named as Hyper heuristic Evolutionary Approach with Recursive and Partition Trees (HEARpart). The proposed approach is validated on 30 datasets. It is statistically proved that HEARpart performs significantly better than WEKA’s J48 algorithm in terms of error rate, F-measure, and tree size. Further, the suggested hyper heuristic algorithm constructs significantly comprehensible models as compared to WEKA’s J48, CART and other similar decision tree construction strategies. The results show that the accuracy achieved by the hyper heuristic approach is slightly less as compared to the other comparative approaches.

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Kalaghatgi, Prabhav, Nico Pfeifer, and Thomas Lengauer. "Family-Joining: A Fast Distance-Based Method for Constructing Generally Labeled Trees." Molecular Biology and Evolution 33, no. 10 (July 19, 2016): 2720–34. http://dx.doi.org/10.1093/molbev/msw123.

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Abstract The widely used model for evolutionary relationships is a bifurcating tree with all taxa/observations placed at the leaves. This is not appropriate if the taxa have been densely sampled across evolutionary time and may be in a direct ancestral relationship, or if there is not enough information to fully resolve all the branching points in the evolutionary tree. In this article, we present a fast distance-based agglomeration method called family-joining (FJ) for constructing so-called generally labeled trees in which taxa may be placed at internal vertices and the tree may contain polytomies. FJ constructs such trees on the basis of pairwise distances and a distance threshold. We tested three methods for threshold selection, FJ-AIC, FJ-BIC, and FJ-CV, which minimize Akaike information criterion, Bayesian information criterion, and cross-validation error, respectively. When compared with related methods on simulated data, FJ-BIC was among the best at reconstructing the correct tree across a wide range of simulation scenarios. FJ-BIC was applied to HIV sequences sampled from individuals involved in a known transmission chain. The FJ-BIC tree was found to be compatible with almost all transmission events. On average, internal branches in the FJ-BIC tree have higher bootstrap support than branches in the leaf-labeled bifurcating tree constructed using RAxML. 36% and 25% of the internal branches in the FJ-BIC tree and RAxML tree, respectively, have bootstrap support greater than 70%. To the best of our knowledge the method presented here is the first attempt at modeling evolutionary relationships using generally labeled trees.

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Villalobos-Cid, Manuel, Francisco Salinas, Eduardo I. Kessi-Pérez, Matteo De Chiara, Gianni Liti, Mario Inostroza-Ponta, and Claudio Martínez. "Comparison of Phylogenetic Tree Topologies for Nitrogen Associated Genes Partially Reconstruct the Evolutionary History of Saccharomyces cerevisiae." Microorganisms 8, no. 1 (December 23, 2019): 32. http://dx.doi.org/10.3390/microorganisms8010032.

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Massive sequencing projects executed in Saccharomyces cerevisiae have revealed in detail its population structure. The recent “1002 yeast genomes project” has become the most complete catalogue of yeast genetic diversity and a powerful resource to analyse the evolutionary history of genes affecting specific phenotypes. In this work, we selected 22 nitrogen associated genes and analysed the sequence information from the 1011 strains of the “1002 yeast genomes project”. We constructed a total evidence (TE) phylogenetic tree using concatenated information, which showed a 27% topology similarity with the reference (REF) tree of the “1002 yeast genomes project”. We also generated individual phylogenetic trees for each gene and compared their topologies, identifying genes with similar topologies (suggesting a shared evolutionary history). Furthermore, we pruned the constructed phylogenetic trees to compare the REF tree topology versus the TE tree and the individual genes trees, considering each phylogenetic cluster/subcluster within the population, observing genes with cluster/subcluster topologies of high similarity to the REF tree. Finally, we used the pruned versions of the phylogenetic trees to compare four strains considered as representatives of S. cerevisiae clean lineages, observing for 15 genes that its cluster topologies match 100% the REF tree, supporting that these strains represent main lineages of yeast population. Altogether, our results showed the potential of tree topologies comparison for exploring the evolutionary history of a specific group of genes.

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Hong, Tzung-Pei, Ke-Yuan Huang, and Wen-Yang Lin. "Adversarial Search by Evolutionary Computation." Evolutionary Computation 9, no. 3 (September 2001): 371–85. http://dx.doi.org/10.1162/106365601750406046.

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In this paper, we consider the problem of finding good next moves in two-player games. Traditional search algorithms, such as minimax and α-β pruning, suffer great temporal and spatial expansion when exploring deeply into search trees to find better next moves. The evolution of genetic algorithms with the ability to find global or near global optima in limited time seems promising, but they are inept at finding compound optima, such as the minimax in a game-search tree. We thus propose a new genetic algorithm-based approach that can find a good next move by reserving the board evaluation values of new offspring in a partial game-search tree. Experiments show that solution accuracy and search speed are greatly improved by our algorithm.

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Cazaux, Bastien, Guillaume Castel, and Eric Rivals. "AQUAPONY: visualization and interpretation of phylogeographic information on phylogenetic trees." Bioinformatics 35, no. 17 (January 14, 2019): 3163–65. http://dx.doi.org/10.1093/bioinformatics/btz011.

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Abstract Motivation The visualization and interpretation of evolutionary spatiotemporal scenarios is broadly and increasingly used in infectious disease research, ecology or agronomy. Using probabilistic frameworks, well-known tools can infer from molecular data ancestral traits for internal nodes in a phylogeny, and numerous phylogenetic rendering tools can display such evolutionary trees. However, visualizing such ancestral information and its uncertainty on the tree remains tedious. For instance, ancestral nodes can be associated to several geographical annotations with close probabilities and thus, several migration or transmission scenarios exist. Results We expose a web-based tool, named AQUAPONY, that facilitates such operations. Given an evolutionary tree with ancestral (e.g. geographical) annotations, the user can easily control the display of ancestral information on the entire tree or a subtree, and can view alternative phylogeographic scenarios along a branch according to a chosen uncertainty threshold. AQUAPONY interactively visualizes the tree and eases the objective interpretation of evolutionary scenarios. AQUAPONY’s implementation makes it highly responsive to user interaction, and instantaneously updates the tree visualizations even for large trees (which can be exported as image files). Availability and implementation AQUAPONY is coded in JavaScript/HTML, available under Cecill license, and can be freely used at http://www.atgc-montpellier.fr/aquapony/.

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Sundermann, Linda K., Jeff Wintersinger, Gunnar Rätsch, Jens Stoye, and Quaid Morris. "Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine." PLOS Computational Biology 17, no. 1 (January 19, 2021): e1008400. http://dx.doi.org/10.1371/journal.pcbi.1008400.

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Tumors contain multiple subpopulations of genetically distinct cancer cells. Reconstructing their evolutionary history can improve our understanding of how cancers develop and respond to treatment. Subclonal reconstruction methods cluster mutations into groups that co-occur within the same subpopulations, estimate the frequency of cells belonging to each subpopulation, and infer the ancestral relationships among the subpopulations by constructing a clone tree. However, often multiple clone trees are consistent with the data and current methods do not efficiently capture this uncertainty; nor can these methods scale to clone trees with a large number of subclonal populations. Here, we formalize the notion of a partially-defined clone tree (partial clone tree for short) that defines a subset of the pairwise ancestral relationships in a clone tree, thereby implicitly representing the set of all clone trees that have these defined pairwise relationships. Also, we introduce a special partial clone tree, the Maximally-Constrained Ancestral Reconstruction (MAR), which summarizes all clone trees fitting the input data equally well. Finally, we extend commonly used clone tree validity conditions to apply to partial clone trees and describe SubMARine, a polynomial-time algorithm producing the subMAR, which approximates the MAR and guarantees that its defined relationships are a subset of those present in the MAR. We also extend SubMARine to work with subclonal copy number aberrations and define equivalence constraints for this purpose. Further, we extend SubMARine to permit noise in the estimates of the subclonal frequencies while retaining its validity conditions and guarantees. In contrast to other clone tree reconstruction methods, SubMARine runs in time and space that scale polynomially in the number of subclones. We show through extensive noise-free simulation, a large lung cancer dataset and a prostate cancer dataset that the subMAR equals the MAR in all cases where only a single clone tree exists and that it is a perfect match to the MAR in most of the other cases. Notably, SubMARine runs in less than 70 seconds on a single thread with less than one Gb of memory on all datasets presented in this paper, including ones with 50 nodes in a clone tree. On the real-world data, SubMARine almost perfectly recovers the previously reported trees and identifies minor errors made in the expert-driven reconstructions of those trees. The freely-available open-source code implementing SubMARine can be downloaded at https://github.com/morrislab/submarine.

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Jofré, Paula, and Payel Das. "Galactic Phylogenetics." Proceedings of the International Astronomical Union 13, S334 (July 2017): 308–9. http://dx.doi.org/10.1017/s1743921317010791.

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AbstractPhylogenetics is a widely used concept in evolutionary biology. It is the reconstruction of evolutionary history by building trees that represent branching patterns and sequences. These trees represent shared history, and it is our contention that this approach can be employed in the analysis of Galactic history. In Galactic archaeology the shared environment is the interstellar medium in which stars form and provides the basis for tree-building as a methodological tool. Using elemental abundances of solar-type stars as a proxy for DNA, we built such an evolutionary tree to study the chemical evolution of the solar neighbourhood and published in Jofré15 et al. (2017). In this proceeding we summarise these results and discuss future prospects.

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Huang, Sophia, and Justen B. Whittall. "A Tree of Trees: Using Campus Tree Diversity to Integrate Molecular, Organismal, and Evolutionary Biology." American Biology Teacher 80, no. 2 (February 1, 2018): 144–51. http://dx.doi.org/10.1525/abt.2018.80.2.144.

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The tree of life provides a fundamental roadmap to understanding biodiversity, yet requires integration across scales of the biological hierarchy and a unique set of tree thinking skills. This combination can be challenging for undergraduates at the introductory level because of their preconceptions regarding distinct fields of biology compounded by the unique structure of phylogenetic trees. To address these two challenges while providing an undergraduate research opportunity, we developed an activity for introductory biology students that integrates molecular, organismal, and evolutionary biology. This activity relies on woody plant identification, comparative morphology, and DNA sequence analysis to teach students how to reconstruct and interpret phylogenetic trees. After building separate phylogenetic hypotheses using morphological characters and molecular data, they compare their results with a master Tree of Trees to identify instances of homology and homoplasy. After delivering this activity, the majority of students scored the activity as “helpful to very helpful” in increasing their understanding of these concepts. Overall, we deliver a framework for developing comparable Tree of Trees–type activities that leverage students' interests in familiar organisms and requires them to span scales of the biological hierarchy while improving their tree thinking skills.

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Jun, Sungbum. "Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing." Sensors 21, no. 8 (April 18, 2021): 2849. http://dx.doi.org/10.3390/s21082849.

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Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.

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Monastersky, R. "Knotty Evolutionary Tree in Plant World." Science News 135, no. 5 (February 4, 1989): 71. http://dx.doi.org/10.2307/3973274.

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HOCHBAUM, DORIT S., and ANU PATHRIA. "Path Costs in Evolutionary Tree Reconstruction." Journal of Computational Biology 4, no. 2 (January 1997): 163–75. http://dx.doi.org/10.1089/cmb.1997.4.163.

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Lundquist, Peter K., Kiran-Kumar Shivaiah, and Roberto Espinoza-Corral. "Lipid droplets throughout the evolutionary tree." Progress in Lipid Research 78 (April 2020): 101029. http://dx.doi.org/10.1016/j.plipres.2020.101029.

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PENNY, DAVID, and M. D. HENDY. "TESTING METHODS OF EVOLUTIONARY TREE CONSTRUCTION." Cladistics 1, no. 3 (June 1985): 266–78. http://dx.doi.org/10.1111/j.1096-0031.1985.tb00427.x.

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Warnow, T. J. "Tree Compatibility and Inferring Evolutionary History." Journal of Algorithms 16, no. 3 (May 1994): 388–407. http://dx.doi.org/10.1006/jagm.1994.1018.

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Kannan, Sampath K., Eugene L. Lawler, and Tandy J. Warnow. "Determining the Evolutionary Tree Using Experiments." Journal of Algorithms 21, no. 1 (July 1996): 26–50. http://dx.doi.org/10.1006/jagm.1996.0035.

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Offner, Susan. "Using Great Ape Phylogeny to Teach Evolutionary Thinking." American Biology Teacher 78, no. 3 (March 1, 2016): 263–65. http://dx.doi.org/10.1525/abt.2016.78.3.263.

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A simple phylogenetic tree of the great apes provides many important teaching opportunities, both in the general skill of reading phylogenetic trees and in using them to explore evolutionary relationships.

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Hamel, L., N. Nahar, M. S. Poptsova, O. Zhaxybayeva, and J. P. Gogarten. "Unsupervised Learning in Detection of Gene Transfer." Journal of Biomedicine and Biotechnology 2008 (2008): 1–7. http://dx.doi.org/10.1155/2008/472719.

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The tree representation as a model for organismal evolution has been in use since before Darwin. However, with the recent unprecedented access to biomolecular data, it has been discovered that, especially in the microbial world, individual genes making up the genome of an organism give rise to different and sometimes conflicting evolutionary tree topologies. This discovery calls into question the notion of a single evolutionary tree for an organism and gives rise to the notion of an evolutionary consensus tree based on the evolutionary patterns of the majority of genes in a genome embedded in a network of gene histories. Here, we discuss an approach to the analysis of genomic data of multiple genomes using bipartition spectral analysis and unsupervised learning. An interesting observation is that genes within genomes that have evolutionary tree topologies, which are in substantial conflict with the evolutionary consensus tree of an organism, point to possible horizontal gene transfer events which often delineate significant evolutionary events.

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Wang, Yaxuan, Zhen Cao, Huw A. Ogilvie, and Luay Nakhleh. "Phylogenomic assessment of the role of hybridization and introgression in trait evolution." PLOS Genetics 17, no. 8 (August 18, 2021): e1009701. http://dx.doi.org/10.1371/journal.pgen.1009701.

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Trait evolution among a set of species—a central theme in evolutionary biology—has long been understood and analyzed with respect to a species tree. However, the field of phylogenomics, which has been propelled by advances in sequencing technologies, has ushered in the era of species/gene tree incongruence and, consequently, a more nuanced understanding of trait evolution. For a trait whose states are incongruent with the branching patterns in the species tree, the same state could have arisen independently in different species (homoplasy) or followed the branching patterns of gene trees, incongruent with the species tree (hemiplasy). Another evolutionary process whose extent and significance are better revealed by phylogenomic studies is gene flow between different species. In this work, we present a phylogenomic method for assessing the role of hybridization and introgression in the evolution of polymorphic or monomorphic binary traits. We apply the method to simulated evolutionary scenarios to demonstrate the interplay between the parameters of the evolutionary history and the role of introgression in a binary trait’s evolution (which we call xenoplasy). Very importantly, we demonstrate, including on a biological data set, that inferring a species tree and using it for trait evolution analysis in the presence of gene flow could lead to misleading hypotheses about trait evolution.

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McCullough, Erin L., Lauren Verdeflor, Alaina Weinsztok, Jason R. Wiles, and Steve Dorus. "Exploratory Activities for Understanding Evolutionary Relationships Depicted by Phylogenetic Trees: United but Diverse." American Biology Teacher 82, no. 5 (May 1, 2020): 333–37. http://dx.doi.org/10.1525/abt.2020.82.5.333.

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Evolution explains both the unity and the diversity of all organisms, and developing students' ability to represent and communicate evolutionary relationships is an important component of a complete biology education. We present a series of student-centered, exploratory activities to help students develop their tree-thinking skills. In these activities, students use complementary phenotypic and molecular data to explore how to build phylogenetic trees and interpret the evolutionary relationships they represent. This learning module is designed to engage students in the process of science, provide them with active learning experiences using online bioinformatics tools, and foster their appreciation for the evolutionary connections across the tree of life.

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Taylor, Greg. "EVOLUTIONARY HIERARCHICAL CREDIBILITY." ASTIN Bulletin 48, no. 1 (November 2, 2017): 339–74. http://dx.doi.org/10.1017/asb.2017.31.

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AbstractThe hierarchical credibility model was introduced, and extended, in the 70s and early 80s. It deals with the estimation of parameters that characterize the nodes of a tree structure. That model is limited, however, by the fact that its parameters are assumed fixed over time. This causes the model's parameter estimates to track the parameters poorly when the latter are subject to variation over time. This paper seeks to remove this limitation by assuming the parameters in question to follow a process akin to a random walk over time, producing an evolutionary hierarchical model. The specific form of the model is compatible with the use of the Kalman filter for parameter estimation and forecasting. The application of the Kalman filter is conceptually straightforward, but the tree structure of the model parameters can be extensive, and some effort is required to retain organization of the updating algorithm. This is achieved by suitable manipulation of the graph associated with the tree. The graph matrix then appears in the matrix calculations inherent in the Kalman filter. A numerical example is included to illustrate the application of the filter to the model.

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Mitov, Venelin, Krzysztof Bartoszek, and Tanja Stadler. "Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models." Proceedings of the National Academy of Sciences 116, no. 34 (August 2, 2019): 16921–26. http://dx.doi.org/10.1073/pnas.1813823116.

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Phylogenetic comparative methods are widely used to understand and quantify the evolution of phenotypic traits, based on phylogenetic trees and trait measurements of extant species. Such analyses depend crucially on the underlying model. Gaussian phylogenetic models like Brownian motion and Ornstein–Uhlenbeck processes are the workhorses of modeling continuous-trait evolution. However, these models fit poorly to big trees, because they neglect the heterogeneity of the evolutionary process in different lineages of the tree. Previous works have addressed this issue by introducing shifts in the evolutionary model occurring at inferred points in the tree. However, for computational reasons, in all current implementations, these shifts are “intramodel,” meaning that they allow jumps in 1 or 2 model parameters, keeping all other parameters “global” for the entire tree. There is no biological reason to restrict a shift to a single model parameter or, even, to a single type of model. Mixed Gaussian phylogenetic models (MGPMs) incorporate the idea of jointly inferring different types of Gaussian models associated with different parts of the tree. Here, we propose an approximate maximum-likelihood method for fitting MGPMs to comparative data comprising possibly incomplete measurements for several traits from extant and extinct phylogenetically linked species. We applied the method to the largest published tree of mammal species with body- and brain-mass measurements, showing strong statistical support for an MGPM with 12 distinct evolutionary regimes. Based on this result, we state a hypothesis for the evolution of the brain–body-mass allometry over the past 160 million y.

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Pasternak, J. J., and B. R. Glick. "Molecular evolutionary analyses of the small and large subunits of ribulose-1,5-bisphosphate carboxylase/oxygenase." Canadian Journal of Botany 70, no. 4 (April 1, 1992): 715–23. http://dx.doi.org/10.1139/b92-092.

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The molecular evolution of the amino acid sequences of the mature small and large subunits of ribulose-1,5-bisphosphate carboxylase/oxygense (Rubisco) was determined. The dataset for each subunit consisted of sequences from 39 different taxa of which 22 are represented with sequence information for both subunits. Phylogenetic trees were reconstructed using distance matrix, parsimony and simultaneous alignment and phylogeny methods. For the small subunit, the latter two methods produced similar trees that differed from the topology of the distance matrix tree. For the large subunit, each of the three tree-building methods yielded a distinct tree. Except for the distance matrix small subunit tree, the tree-building methods produced topologies for the small and large subunit sequences from the nonflowering plant taxa that, for the most part, agree with current taxonomic schemes. With the full datasets, the lack of consistency both among the various trees and with conventional taxonomic relationships was most evident with the Rubisco sequences from angiosperms. It is unlikely that current tree-building methods will be able to reconstruct an unambiguous molecular evolution of either of the Rubisco subunits. Molecular trees, regardless of methodology, showed similar topologies for the small and large subunits from the 22 taxa from which both subunits have been sequenced, indicating that the subunits have changed to the same extent over time. In this case, similar trees were formed because only 4 of the 22 taxa were from dicots. Key words: ribulose-1,5-bisphosphate carboxylase/oxygenase, amino acid sequence, molecular evolution, phyletic trees.

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Scholz, Guillaume E., Andrei-Alin Popescu, Martin I. Taylor, Vincent Moulton, and Katharina T. Huber. "OSF-Builder: A New Tool for Constructing and Representing Evolutionary Histories Involving Introgression." Systematic Biology 68, no. 5 (January 22, 2019): 717–29. http://dx.doi.org/10.1093/sysbio/syz004.

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Abstract Introgression is an evolutionary process which provides an important source of innovation for evolution. Although various methods have been used to detect introgression, very few methods are currently available for constructing evolutionary histories involving introgression. In this article, we propose a new method for constructing such evolutionary histories whose starting point is a species forest (consisting of a collection of lineage trees, usually arising as a collection of clades or monophyletic groups in a species tree), and a gene tree for a specific allele of interest, or allele tree for short. Our method is based on representing introgression in terms of a certain “overlay” of the allele tree over the lineage trees, called an overlaid species forest (OSF). OSFs are similar to phylogenetic networks although a key difference is that they typically have multiple roots because each monophyletic group in the species tree has a different point of origin. Employing a new model for introgression, we derive an efficient algorithm for building OSFs called OSF-Builder that is guaranteed to return an optimal OSF in the sense that the number of potential introgression events is minimized. As well as using simulations to assess the performance of OSF-Builder, we illustrate its use on a butterfly data set in which introgression has been previously inferred. The OSF-Builder software is available for download from https://www.uea.ac.uk/computing/software/OSF-Builder.

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Francis, A., K. T. Huber, and V. Moulton. "Tree-Based Unrooted Phylogenetic Networks." Bulletin of Mathematical Biology 80, no. 2 (December 13, 2017): 404–16. http://dx.doi.org/10.1007/s11538-017-0381-3.

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Abstract Phylogenetic networks are a generalization of phylogenetic trees that are used to represent non-tree-like evolutionary histories that arise in organisms such as plants and bacteria, or uncertainty in evolutionary histories. An unrooted phylogenetic network on a non-empty, finite set X of taxa, or network, is a connected, simple graph in which every vertex has degree 1 or 3 and whose leaf set is X. It is called a phylogenetic tree if the underlying graph is a tree. In this paper we consider properties of tree-based networks, that is, networks that can be constructed by adding edges into a phylogenetic tree. We show that although they have some properties in common with their rooted analogues which have recently drawn much attention in the literature, they have some striking differences in terms of both their structural and computational properties. We expect that our results could eventually have applications to, for example, detecting horizontal gene transfer or hybridization which are important factors in the evolution of many organisms.

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Nye, Tom M. W., Brad J. C. Baxter, and Walter R. Gilks. "A Covariance Matrix Inversion Problem arising from the Construction of Phylogenetic Trees." LMS Journal of Computation and Mathematics 10 (2007): 119–31. http://dx.doi.org/10.1112/s1461157000001327.

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AbstractWe describe an efficient algorithm for the inversion of covariance matrices that arise in the context of phylogenetic tree construction. Phylogenetic trees describe the evolutionary relationships between species, and their construction is computationally demanding. Many approaches involve the symmetric matrix of evolutionary distances between species. Regarding these distances as random variables, the corresponding set of variances and covariances form a rank-4 tensor, and the inner-product defined by its inverse can be used to assign statistical scores to candidate trees. We describe a natural set of assumptions for the phylogenetic tree under construction, and show how under these assumptions the covariance tensor for a tree with n leaves can be inverted in O(n2) operations. In addition to presenting the inversion algorithm, we hope this article will open algebraic and computational problems from the field of phylogeny to a wider audience.

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Christensen, Sarah, Juho Kim, Nicholas Chia, Oluwasanmi Koyejo, and Mohammed El-Kebir. "Detecting evolutionary patterns of cancers using consensus trees." Bioinformatics 36, Supplement_2 (December 2020): i684—i691. http://dx.doi.org/10.1093/bioinformatics/btaa801.

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Abstract Motivation While each cancer is the result of an isolated evolutionary process, there are repeated patterns in tumorigenesis defined by recurrent driver mutations and their temporal ordering. Such repeated evolutionary trajectories hold the potential to improve stratification of cancer patients into subtypes with distinct survival and therapy response profiles. However, current cancer phylogeny methods infer large solution spaces of plausible evolutionary histories from the same sequencing data, obfuscating repeated evolutionary patterns. Results To simultaneously resolve ambiguities in sequencing data and identify cancer subtypes, we propose to leverage common patterns of evolution found in patient cohorts. We first formulate the Multiple Choice Consensus Tree problem, which seeks to select a tumor tree for each patient and assign patients into clusters in such a way that maximizes consistency within each cluster of patient trees. We prove that this problem is NP-hard and develop a heuristic algorithm, Revealing Evolutionary Consensus Across Patients (RECAP), to solve this problem in practice. Finally, on simulated data, we show RECAP outperforms existing methods that do not account for patient subtypes. We then use RECAP to resolve ambiguities in patient trees and find repeated evolutionary trajectories in lung and breast cancer cohorts. Availability and implementation https://github.com/elkebir-group/RECAP. Supplementary information Supplementary data are available at Bioinformatics online.

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Zhang, Byoung-Tak, Peter Ohm, and Heinz Mühlenbein. "Evolutionary Induction of Sparse Neural Trees." Evolutionary Computation 5, no. 2 (June 1997): 213–36. http://dx.doi.org/10.1162/evco.1997.5.2.213.

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This paper is concerned with the automatic induction of parsimonious neural networks. In contrast to other program induction situations, network induction entails parametric learning as well as structural adaptation. We present a novel representation scheme called neural trees that allows efficient learning of both network architectures and parameters by genetic search. A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle. The method is successfully applied to the induction of higher order neural trees while still keeping the resulting structures sparse to ensure good generalization performance. Empirical results are provided on two chaotic time series prediction problems of practical interest.

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Mooers, Arne O., and Stephen B. Heard. "Inferring Evolutionary Process from Phylogenetic Tree Shape." Quarterly Review of Biology 72, no. 1 (March 1997): 31–54. http://dx.doi.org/10.1086/419657.

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Farach, Martin, and Mikkel Thorup. "Sparse Dynamic Programming for Evolutionary-Tree Comparison." SIAM Journal on Computing 26, no. 1 (February 1997): 210–30. http://dx.doi.org/10.1137/s0097539794262422.

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Karabadji, Nour El Islem, Hassina Seridi, Fouad Bousetouane, Wajdi Dhifli, and Sabeur Aridhi. "An evolutionary scheme for decision tree construction." Knowledge-Based Systems 119 (March 2017): 166–77. http://dx.doi.org/10.1016/j.knosys.2016.12.011.

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Shakya, Subir B., Jérôme Fuchs, Jean-Marc Pons, and Frederick H. Sheldon. "Tapping the woodpecker tree for evolutionary insight." Molecular Phylogenetics and Evolution 116 (November 2017): 182–91. http://dx.doi.org/10.1016/j.ympev.2017.09.005.

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Outlaw, Diana C., and Robert E. Ricklefs. "Rerooting the evolutionary tree of malaria parasites." Proceedings of the National Academy of Sciences 108, no. 32 (July 5, 2011): 13183–87. http://dx.doi.org/10.1073/pnas.1109153108.

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Petit, Rémy J., and Arndt Hampe. "Some Evolutionary Consequences of Being a Tree." Annual Review of Ecology, Evolution, and Systematics 37, no. 1 (December 2006): 187–214. http://dx.doi.org/10.1146/annurev.ecolsys.37.091305.110215.

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Badger, Jonathan, Paul Kearney, Ming Li, John Tsang, and Tao Jiang. "Selecting the branches for an evolutionary tree." Journal of Algorithms 51, no. 1 (April 2004): 1–14. http://dx.doi.org/10.1016/s0196-6774(03)00086-5.

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Ridley, Mark. "Major evolutionary changes and incorrect tree reconstruction." Journal of Natural History 25, no. 3 (June 1991): 809. http://dx.doi.org/10.1080/00222939100770521.

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Marazzi, Brigitte, Cécile Ané, Marcelo F. Simon, Alfonso Delgado-Salinas, Melissa Luckow, and Michael J. Sanderson. "LOCATING EVOLUTIONARY PRECURSORS ON A PHYLOGENETIC TREE." Evolution 66, no. 12 (August 6, 2012): 3918–30. http://dx.doi.org/10.1111/j.1558-5646.2012.01720.x.

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MacDonald, Teresa, and E. O. Wiley. "Communicating Phylogeny: Evolutionary Tree Diagrams in Museums." Evolution: Education and Outreach 5, no. 1 (March 2012): 14–28. http://dx.doi.org/10.1007/s12052-012-0387-0.

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Journal articles on the topic 'Evolutionary tree'

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