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13 results for “searching tree”
Experimental data of the paper "Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces"
<p>This data set contains the code of our planner and of the planner that was used as baseline, the benchmark set that was used to perform experiments as well as the parsed values and basic reports that are reported in the paper. More information can be found in the README that is also included.</p>
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50). in A New Thaumastocyoninae (Amphicyonidae, Carnivora) From The Early Miocene Of Tuchořice, The Czech Republic
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50).
Dataset of 'Search for top-down and bottom-up drivers of latitudinal trends in insect herbivory in oak trees in Europe'
<p>This file correspond to the dataset that has being used in the article ‘Search for top-down and bottom-up drivers of latitudinal trends in insect herbivory in oak trees in Europe’ by Elena Valdés-Correcher et al. in Global Ecology and Biogeography.</p>
Data from: The influence of the number of tree searches on maximum likelihood inference in phylogenomics
<p>Maximum likelihood (ML) phylogenetic inference is widely used in phylogenomics. As heuristic searches most likely find suboptimal trees, it is recommended to conduct multiple (e.g., ten) tree searches in phylogenetic analyses. However, beyond its positive role, how and to what extent multiple tree searches aid ML phylogenetic inference remains poorly explored. Here, we found that a random starting tree was not as effective as the BioNJ and parsimony starting trees in inferring ML gene tree and that RAxML-NG and PhyML were less sensitive to different starting trees than IQ-TREE. We then examined the effect of the number of tree searches on ML tree inference with IQ-TREE and RAxML-NG, by running 100 tree searches on 19,414 gene alignments from 15 animal, plant, and fungal phylogenomic datasets. We found that the number of tree searches substantially impacted the recovery of the best-of-100 ML gene tree topology among 100 searches for a given ML program. In addition, all of the concatenation-based trees were topologically identical if the number of tree searches was ≥ 10. Quartet-based ASTRAL trees inferred from 1 to 80 tree searches differed topologically from those inferred from 100 tree searches for 6 /15 phylogenomic datasets. Lastly, our simulations showed that gene alignments with lower difficulty scores had a higher chance of finding the best-of-100 gene tree topology and were more likely to yield the correct trees.</p>
Data from: The influence of the number of tree searches on maximum likelihood inference in phylogenomics
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Data from: Stalking the fourth domain in metagenomic data: searching for, discovering, and interpreting novel, deep branches in phylogenetic trees of phylogenetic marker genes
BACKGROUND: Most of our knowledge about the ancient evolutionary history of organisms has been derived from data associated with specific known organisms (i.e., organisms that we can study directly such as plants, metazoans, and culturable microbes). Recently, however, a new source of data for such studies has arrived: DNA sequence data generated directly from environmental samples. Such metagenomic data has enormous potential in a variety of areas including, as we argue here, in studies of very early events in the evolution of gene families and of species. METHODOLOGY/PRINCIPAL FINDINGS: We designed and implemented new methods for analyzing metagenomic data and used them to search the Global Ocean Sampling (GOS) Expedition data set for novel lineages in three gene families commonly used in phylogenetic studies of known and unknown organisms: small subunit rRNA and the recA and rpoB superfamilies. Though the methods available could not accurately identify very deeply branched ss-rRNAs (largely due to difficulties in making robust sequence alignments for novel rRNA fragments), our analysis revealed the existence of multiple novel branches in the recA and rpoB gene families. Analysis of available sequence data likely from the same genomes as these novel recA and rpoB homologs was then used to further characterize the possible organismal source of the novel sequences. CONCLUSIONS/SIGNIFICANCE: Of the novel recA and rpoB homologs identified in the metagenomic data, some likely come from uncharacterized viruses while others may represent ancient paralogs not yet seen in any cultured organism. A third possibility is that some come from novel cellular lineages that are only distantly related to any organisms for which sequence data is currently available. If there exist any major, but so-far-undiscovered, deeply branching lineages in the tree of life, we suggest that methods such as those described herein currently offer the best way to search for them.
Implementing YewPar: a Framework for Parallel Tree Search [Dataset]
<p>Dataset and scripts for "Implementing YewPar: a Framework for Parallel Tree Search"</p>
FIGURE 1. Topology showing the most parsimonious tree obtained from a heuristic search with 1,000 in A new species of Microthyrium from Yunnan, China
FIGURE 1. Topology showing the most parsimonious tree obtained from a heuristic search with 1,000 random taxon additions of the combined dataset of SSU and LSU sequences alignment using PAUP v. 4.0b10. The scale bar shows 10 changes. Bootstrap support values for maximum parsimony (MP) and maximum likelihood (ML) greater than 50% above the nodes. The values below the nodes are Bayesian posterior probabilities above 0.95. Hyphen ("-") indicates a value lower than 50% (BS) or 0.90 (PP). The original isolate numbers are noted after the species names. The tree is rooted to Schismatomma decolorans.
FIGURE 1. The most parsimonious trees obtained from a heuristic search with 1000 in Phyllosticta species from banana (Musa sp.) in Chongqing and Guizhou Provinces, China
FIGURE 1. The most parsimonious trees obtained from a heuristic search with 1000 random taxon additions of the LSU sequences using PAUP v. 4.0b10. The scale bar shows 5 changes. Bootstrap support values for maximum parsimony (MP) and Bayesian posterior probabilities above 0.90are shown. A hyphens (–) indicates the value lower than 50% (BS) or 0.90 (PP). The tree is rooted to Botryosphaeria dothidea. Ex-type/ex-epitype isolates are marked by an asterisk *. Novel sequences are in boldface.
Data from: Search for top-down and bottom-up drivers of latitudinal trends in insect herbivory in oak trees in Europe
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Data from: Stalking the fourth domain in metagenomic data: searching for, discovering, and interpreting novel, deep branches in phylogenetic trees of phylogenetic marker genes
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Artifact for paper "Monte Carlo Tree Search for Priced Timed Automata"
<p>Artifact for paper "Monte Carlo Tree Search for Priced Timed Automata"</p>
Data from: Bias in tree searches and its consequences for measuring group supports
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.