Maximum weighted spanning tree - PMC

Kathrin Kühnhammer, Joost van Haren, Angelika Kübert, Kinzie Bailey, Maren Dubbert, Jia Hu, S. Nemiah Ladd, Laura K. Meredith, Christiane Werner, Matthias Beyer

Sci Total Environ. 2023 Oct 1; 893: 164763. doi: 10.1016/j.scitotenv.2023.164763

PMCID:: PMC10331952

Abstract Article

Select item 114808635.

Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data

Leah L. Weber, Derek Reiman, Mrinmoy S. Roddur, Yuanyuan Qi, Mohammed El-Kebir, Aly A. Khan

Cell Genom. 2024 Sep 11; 4(9): 100637. Published online 2024 Aug 28. doi: 10.1016/j.xgen.2024.100637

PMCID:: PMC11480863

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Reliable Route Selection for Wireless Sensor Networks with Connection Failure Uncertainties

Jianhua Lyu, Yiran Ren, Zeeshan Abbas, Baili Zhang

Sensors (Basel) 2021 Nov; 21(21): 7254. Published online 2021 Oct 31. doi: 10.3390/s21217254

PMCID:: PMC8588549

Abstract Article PDF–4.0M

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The Limits of the Constant-rate Birth–Death Prior for Phylogenetic Tree Topology Inference

Mark P Khurana, Neil Scheidwasser-Clow, Matthew J Penn, Samir Bhatt, David A Duchêne

Syst Biol. 2024 Jan; 73(1): 235–246. Published online 2023 Dec 28. doi: 10.1093/sysbio/syad075

PMCID:: PMC11129600

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Filtration of Gene Trees From 9,000 Exons, Introns, and UCEs Disentangles Conflicting Phylogenomic Relationships in Tree Frogs (Hylidae)

Carl R Hutter, William Duellman

Genome Biol Evol. 2023 May; 15(5): evad070. Published online 2023 May 2. doi: 10.1093/gbe/evad070

PMCID:: PMC10171231

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Individual canopy tree species maps for the National Ecological Observatory Network

Ben G. Weinstein, Sergio Marconi, Alina Zare, Stephanie A. Bohlman, Aditya Singh, Sarah J. Graves, Lukas Magee, Daniel J. Johnson, Sydne Record, Vanessa E. Rubio, Nathan G. Swenson, Philip Townsend, Thomas T. Veblen, Robert A. Andrus, Ethan P. White

PLoS Biol. 2024 Jul; 22(7): e3002700. Published online 2024 Jul 16. doi: 10.1371/journal.pbio.3002700

PMCID:: PMC11251727

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TreeTerminus —creating transcript trees using inferential replicate counts

Noor Pratap Singh, Michael I. Love, Rob Patro

iScience. 2023 Jun 16; 26(6): 106961. Published online 2023 May 25. doi: 10.1016/j.isci.2023.106961

PMCID:: PMC10291472

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A New Phylogenomic Approach For Quantifying Horizontal Gene Transfer Trends in Prokaryotes

Eliran Avni, Sagi Snir

Sci Rep. 2020; 10: 12425. Published online 2020 Jul 24. doi: 10.1038/s41598-020-62446-5

PMCID:: PMC7381616

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Investigating old‐growth ponderosa pine physiology using tree‐rings, δ¹³C, δ¹⁸O, and a process‐based model

Danielle E. M. Ulrich, Christopher Still, J. Renée Brooks, Youngil Kim, Frederick C. Meinzer

Ecology. 2019 Jun; 100(6): e02656. Published online 2019 Apr 15. doi: 10.1002/ecy.2656

PMCID:: PMC6645703

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Dynamics of investor spanning trees around dot-com bubble

Sindhuja Ranganathan, Mikko Kivelä, Juho Kanniainen

PLoS One. 2018; 13(6): e0198807. Published online 2018 Jun 13. doi: 10.1371/journal.pone.0198807

PMCID:: PMC5999117

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Global atmospheric methane uptake by upland tree woody surfaces

Vincent Gauci, Sunitha Rao Pangala, Alexander Shenkin, Josep Barba, David Bastviken, Viviane Figueiredo, Carla Gomez, Alex Enrich-Prast, Emma Sayer, Tainá Stauffer, Bertie Welch, Dafydd Elias, Niall McNamara, Myles Allen, Yadvinder Malhi

Nature. 2024; 631(8022): 796–800. Published online 2024 Jul 24. doi: 10.1038/s41586-024-07592-w

PMCID:: PMC11269171

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Scelestial: Fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm

Mohammad-Hadi Foroughmand-Araabi, Sama Goliaei, Alice C. McHardy

PLoS Comput Biol. 2022 Aug; 18(8): e1009100. Published online 2022 Aug 11. doi: 10.1371/journal.pcbi.1009100

PMCID:: PMC9426887

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Parameter, noise, and tree topology effects in tumor phylogeny inference

Kiran Tomlinson, Layla Oesper

BMC Med Genomics. 2019; 12(Suppl 10): 184. Published online 2019 Dec 23. doi: 10.1186/s12920-019-0626-0

PMCID:: PMC6927103

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Summarizing the solution space in tumor phylogeny inference by multiple consensus trees

Nuraini Aguse, Yuanyuan Qi, Mohammed El-Kebir

Bioinformatics. 2019 Jul; 35(14): i408–i416. Published online 2019 Jul 5. doi: 10.1093/bioinformatics/btz312

PMCID:: PMC6612807

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TreeMerge: a new method for improving the scalability of species tree estimation methods

Erin K Molloy, Tandy Warnow

Bioinformatics. 2019 Jul; 35(14): i417–i426. Published online 2019 Jul 5. doi: 10.1093/bioinformatics/btz344

PMCID:: PMC6612878

Abstract Article PDF–444K

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Compositional response of Amazon forests to climate change

Adriane Esquivel‐Muelbert, Timothy R. Baker, Kyle G. Dexter, Simon L. Lewis, Roel J. W. Brienen, Ted R. Feldpausch, Jon Lloyd, Abel Monteagudo‐Mendoza, Luzmila Arroyo, Esteban Álvarez-Dávila, Niro Higuchi, Beatriz S. Marimon, Ben Hur Marimon-Junior, Marcos Silveira, Emilio Vilanova, Emanuel Gloor, Yadvinder Malhi, Jerôme Chave, Jos Barlow, Damien Bonal, Nallaret Davila Cardozo, Terry Erwin, Sophie Fauset, Bruno Hérault, Susan Laurance, Lourens Poorter, Lan Qie, Clement Stahl, Martin J. P. Sullivan, Hans ter Steege, Vincent Antoine Vos, Pieter A. Zuidema, Everton Almeida, Edmar Almeida de Oliveira, Ana Andrade, Simone Aparecida Vieira, Luiz Aragão, Alejandro Araujo‐Murakami, Eric Arets, Gerardo A. Aymard C, Christopher Baraloto, Plínio Barbosa Camargo, Jorcely G. Barroso, Frans Bongers, Rene Boot, José Luís Camargo, Wendeson Castro, Victor Chama Moscoso, James Comiskey, Fernando Cornejo Valverde, Antonio Carlos Lola da Costa, Jhon del Aguila Pasquel, Anthony Di Fiore, Luisa Fernanda Duque, Fernando Elias, Julien Engel, Gerardo Flores Llampazo, David Galbraith, Rafael Herrera Fernández, Eurídice Honorio Coronado, Wannes Hubau, Eliana Jimenez‐Rojas, Adriano José Nogueira Lima, Ricardo Keichi Umetsu, William Laurance, Gabriela Lopez‐Gonzalez, Thomas Lovejoy, Omar Aurelio Melo Cruz, Paulo S. Morandi, David Neill, Percy Núñez Vargas, Nadir C. Pallqui Camacho, Alexander Parada Gutierrez, Guido Pardo, Julie Peacock, Marielos Peña‐Claros, Maria Cristina Peñuela‐Mora, Pascal Petronelli, Georgia C. Pickavance, Nigel Pitman, Adriana Prieto, Carlos Quesada, Hirma Ramírez‐Angulo, Maxime Réjou‐Méchain, Zorayda Restrepo Correa, Anand Roopsind, Agustín Rudas, Rafael Salomão, Natalino Silva, Javier Silva Espejo, James Singh, Juliana Stropp, John Terborgh, Raquel Thomas, Marisol Toledo, Armando Torres‐Lezama, Luis Valenzuela Gamarra, Peter J. van de Meer, Geertje van der Heijden, Peter van der Hout, Rodolfo Vasquez Martinez, Cesar Vela, Ima Célia Guimarães Vieira, Oliver L. Phillips

Glob Chang Biol. 2019 Jan; 25(1): 39–56. Published online 2018 Nov 8. doi: 10.1111/gcb.14413

PMCID:: PMC6334637

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Multiscale predictors of small tree survival across a heterogeneous tropical landscape

Eileen H. Helmer, Shannon Kay, Humfredo Marcano-Vega, Jennifer S. Powers, Tana E. Wood, Xiaolin Zhu, David Gwenzi, Thomas S. Ruzycki

PLoS One. 2023; 18(3): e0280322. Published online 2023 Mar 15. doi: 10.1371/journal.pone.0280322

PMCID:: PMC10016699

Abstract Article PDF–3.1M

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Figure 4

The hydrophathy minimum spanning tree for the scTIM. The edges of the top level graph (Figure 1) are weighted by the sum of the respective vertex weights. Each vertex is weighted by the maximum over the hydropathy weighted degrees of the corresponding midlevel graph. The complete top level graph can be filtered based on different properties of interest, here hydropathy, to identify likely physical causes for mutation effects.

Seeing the results of a mutation with a vertex weighted hierarchical graph

BMC Proc. 2014;8(Suppl 2):S7-S7.
Figure 4.Maximum Spanning Tree (MST) of the modular graphs (complete surface tessellation).

A) Given an undirected weighted graph (grey), an MST (red) is a subset of edges connecting all the nodes (cycles are not permitted) with the maximum possible total edge weight. B) The sum(MST) is plotted as a function of β ranging the number of modules (rows) and the number of nodes in the global graphs (columns). For the indicated number of modules (16,25,36,64), N_G-dependent fittings are presented (s=slope, q=y-intercept; fitting (red lines) y=sx+q. Adj. R² ≥ 0.99 in all fittings). N. of simulations ≥ 12.

On the prediction of neuronal microscale topology descriptors based on mesoscale recordings

Eur J Neurosci. ;54(6):6147-6167.
Figure 6.Maximum Spanning Tree (MST) of the modular graphs (partial volume tessellation).

A) Given an undirected weighted graph (grey), an MST (red) is a subset of edges connecting all the nodes (cycles are not permitted) with the maximum possible total edge weight. B) The sum(MST) are plotted as a function of β ranging the number of modules (rows) and the number of nodes in the global graphs (columns). For the indicated number of modules (16,25,36,64), N_G-dependent linear fittings are presented (s=slope, q=y-intercept; fitting (red lines) y=sx+q. Adj. R² ≥ 0.99 in all fittings.). N. of simulations ≥ 9.

On the prediction of neuronal microscale topology descriptors based on mesoscale recordings

Eur J Neurosci. ;54(6):6147-6167.
Fig. 2

Network centrality measures from the different analysis techniques. (a)–(d) Full weighted network, (e)–(h) binary network with equal edges, (i)–(m) weighted network thresholded at .005, (n)–(q) binary network thresholded at .005, (r) maximum spanning tree. Average measures of betweenness centrality, node strength, eigenvector centrality, closeness centrality and degree centrality are calculated on the different networks in control (black) and tinnitus (red) groups in the eight frequency bands. The bars represent the standard error about the mean of the data. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Emerging hubs in phantom perception connectomics

Neuroimage Clin. 2016;11:181-194.