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242 results for “maximum likelihood”
Fig. 4. Maximum likelihood tree generated using ITS1-5.8S-ITS2 in Morphological and molecular description of Pallisentis roparensis n. sp. (Acanthocephala: Quadrigyridae) infecting the freshwater cat fish Wallago attu from Ropar Wetland, Punjab, India
Fig. 4. Maximum likelihood tree generated using ITS1-5.8S-ITS2 gene sequence of Pallisentis roparensis and the sequences of related taxa downloaded from GenBank. Numbers near internal nodes show ML bootstrap clade frequencies.
Fig. 3. Maximum likelihood tree generated using 28S in Morphological and molecular description of Pallisentis roparensis n. sp. (Acanthocephala: Quadrigyridae) infecting the freshwater cat fish Wallago attu from Ropar Wetland, Punjab, India
Fig. 3. Maximum likelihood tree generated using 28S rRNA gene sequence of Pallisentis roparensis and the sequences of related taxa downloaded from GenBank. Numbers near internal nodes show ML bootstrap clade frequencies.
Fig. 2. Maximum likelihood tree generated using 18S in Morphological and molecular description of Pallisentis roparensis n. sp. (Acanthocephala: Quadrigyridae) infecting the freshwater cat fish Wallago attu from Ropar Wetland, Punjab, India
Fig. 2. Maximum likelihood tree generated using 18S rRNA gene sequence of Pallisentis roparensis and the sequences of related taxa downloaded from GenBank. Numbers near internal nodes show ML bootstrap clade frequencies.
Fig. 2. Maximum likelihood tree constructed from partial cox1 in Spirometra infection in a captive Samar cobra (Naja samarensis) in the United States: An imported case?
Fig. 2. Maximum likelihood tree constructed from partial cox1 gene sequences of Spirometra samples and related taxa. HKY + G + I was used as the best substitution model. Schistocephalus solidus and Dibothriocephalus nihonkaiensis were used as outgroups. (JPN – Japan; KOR – South Korea; CHI and CHN – China; AUS – Australia; IRA – Iran; USA – United States; THA – Thailand; MMR – Myanmar; TZA – Tanzania; IND – India; VNM – Vietnam; KHM – Cambodia; LAO – Laos; COL – Colombia; NZL – New Zealand; IDN – Indonesia; ROU – Romania; SSD – South Sudan; ETH – Ethiopia; POL – Poland; UKR – Ukraine; FIN – Finland; CHL – Chile; BRA – Brazil).
Fig. 1. Maximum likelihood tree generating from a 399 in Isolation and Characterization of Polymorphic Microsatellite Loci for Caridina cantonensis and Transferability Across Eight Confamilial Species (Atyidae, Decapoda)
Fig. 1. Maximum likelihood tree generating from a 399-bp long COI dataset (GenBank accession no. MH176649-MH176993). SH-alrt/ bootstrap support values are indicated at major nodes. Each coloured notation represents one species.
Fig. 3. Maximum likelihood phylogeny from 882 in A fresh start in ambersnail (Gastropoda: Succineidae) taxonomy: finding a foothold using a widespread species of Oxyloma
Fig. 3. Maximum likelihood phylogeny from 882 bp alignment of 15 LSU sequences presenting only the focal taxa and localities. Ultra-fast bootstrap values indicated behind the nodes supported. Full 51-individual analysis reported in Supp. file 2, Supp. file 3. Type localities indicated with an *.
Fig. 2. Maximum likelihood phylogeny from 690 in A fresh start in ambersnail (Gastropoda: Succineidae) taxonomy: finding a foothold using a widespread species of Oxyloma
Fig. 2. Maximum likelihood phylogeny from 690 bp alignment of 34 COI sequences presenting only the focal taxa and localities. Ultra-fast bootstrap values indicated behind the nodes supported. Full 142-individual analysis reported in Supp. file 2, Supp. file 3. Type localities indicated with an *. Species supported by species delimitation analyses indicated with a hollow circle.
Fig. 1. Maximum likelihood tree inferred from the COI dataset with 1000 in Seven new giant pill-millipede species and numerous new records of the genus Zoosphaerium from Madagascar (Diplopoda, Sphaerotheriida, Arthrosphaeridae)
Fig. 1. Maximum likelihood tree inferred from the COI dataset with 1000 bootstrap pseudoreplicates implementing the GTR + I + G model. Colors representing newly described species of Zoosphaerium: orange = Z. nigrum sp. nov.; green = Z. silens sp. nov.; red = Z. ambatovaky sp. nov.; yellow = Z. beanka sp. nov.; purple = Z. voahangy sp. nov.; blue = Z. masoala sp. nov. Round-cornered rectangles indicate well-supported sister group relationships.
Figure 10. Maximum likelihood tree estimated using Garli 2.0 with 5 in Systematics and Phylogeny of the Australian Cicada Genus Pauropsalta Goding and Froggatt, 1904 and Allied Genera (Hemiptera: Cicadidae: Cicadettini)
Figure 10. Maximum likelihood tree estimated using Garli 2.0 with 5 loci (1 mtDNA and 4 nDNA). Branch support values are bootstrap percentages from 100 non parametric bootstrap replicates. Bootstrap support values ≥ 70 are shown. Molecular voucher numbers are adjacent to species names.
Figure 2 in The maximum likelihood identification method applied to insect morphometric data
Figure 2. Classification based on landmarks (and semilandmarks). Values in percent are the proportions of correct assignments averaged over the complete set of data, mixing the various species, but restricted to the landmark-based method (including here the combination of landmarks and semilandmarks). Each value is shown with an error bar which is its standard deviation. The left part shows the results from the maximum likelihood method applied to the centroid size and to the shape variables. The right part shows the average scores obtained with the Mahalanobis classification. Abbreviation: first PC—the shape variables used were the few first PC of shape variables that the MLi classification selected as the most discriminant set of PC; all PC—the maximum number of PC that could be used by the Mahalanobis classification.
Figure 4 in The maximum likelihood identification method applied to insect morphometric data
Figure 4. Principal component analysis of sandfly data. The factor map of the two first principal components (PC) describing the morphospace of female sandfly data: S. bailyi, S. barraudi and P. stantoni. Unidentified males show a convex hull covering both female S. bailyi and S. barraudi, leaving two specimens outside the female hulls.
Figure 1 in The maximum likelihood identification method applied to insect morphometric data
Figure 1. Measurements on Triatominae. A. Digitization of the contour of the eggs of Triatomini and Rhodniini. B. Landmarks as digitized on the wing of Panstrongylus chinai. C. Traditional measurement of eggs of the tribe Triatomini (Panstrongylus sp. and Triatoma sp., left) and the tribe Rhodniini (Rhodnius sp., right). Abbreviation: op.dim—dimensions of the operculum; max.leng— maximum length; max.dim—maximum diameter.
Figure 3 in The maximum likelihood identification method applied to insect morphometric data
Figure 3. Classification based on harmonics. Values in percent are the proportions of correct assignments averaged over the complete set of data, mixing the various species, but restricted to the outline-based method. Each value is shown with an error bar which is its standard deviation. The left part shows the results from the maximum likelihood method applied to the perimeter of the contour and to the shape variables. The right part shows the average scores obtained with the Mahalanobis classification. Abbreviation: first PC— the shape variables used were the few first PC of shape variables that the MLi classification selected as the most discriminant set of PC; all PC—the maximum number of PC that could be used by the Mahalanobis classification.
FIG. 4. Maximum-likelihood phylogeny for 45 in Phylogenetic Relationships of New World Porcupines (Rodentia, Erethizontidae): Implications for Taxonomy, Morphological Evolution, and Biogeography
FIG. 4. Maximum-likelihood phylogeny for 45 ingroup (erethizontid) terminals; outgroup taxa are not shown. Labeling conventions and nodal support statistics are the same as in figure 3. Capital letters (A, B, C) indicate unnamed clades discussed in the text.
Figure 11. Maximum likelihood phylogram. Bootstrap values greater than 50 in Evolution of cave living in Hawaiian Schrankia (Lepidoptera: Noctuidae) with description of a remarkable new cave species
Figure 11. Maximum likelihood phylogram. Bootstrap values greater than 50% are illustrated on tree as first value; Bayesian posterior probabilities are the second value. Values <50 are not labelled. Colours represent islands (black is Hawaii Island; red is Maui, blue is Oahu; green is Kauai). Asterisks denote flightless individuals. Black circles represent dark-zone morph Schrankia howarthi; grey circles represent twilight-zone individuals. Locality information for individual moths may be found in the Appendix.
Figure 2. A, Tree resulting from maximum likelihood analysis from the 12S in A molecular perspective on the evolutionary affinities of an enigmatic neotropical frog, Allophryne ruthveni
Figure 2. A, Tree resulting from maximum likelihood analysis from the 12S data set using the Hasegawa–Kishino–Yano two parameter model (Hasegawa et al., 1985). Included for comparative purposes are bootstrap support from NJ analyses. Below each resolved branch are indicated percentage bootstrap support in excess of 50% for: ML (100 pseudoreplicates), NJ (1000 pseudoreplicates; Kimura 2-parameter), NJ (1000 pseudoreplicates; Tamura–Nei). B, Strict consensus of three most parsimonious trees (all substitutions weighted equally; tree length = 164 steps). Below each branch are indicated percentage bootstrap support (1000 pseudreplicates) in excess of 50% for: MP (unweighted), MP (stems weighted twice loops), MP (transversions weighted four times transitions). Bremer decay indices (DI) are the final value shown below each resolved branch (for MP unweighted only). A dash indicates bootstrap support of less than 50%.
Fig. 2. Maximum Likelihood species tree from the concatenated 50 in Ultraconserved elements-based phylogenomic systematics of the snake superfamily Elapoidea, with the description of a new Afro-Asian family
Fig. 2. Maximum Likelihood species tree from the concatenated 50 % complete dataset consisting of 4561 loci. Values on the branch indicate Shimodaira Hasegawalike approximate likelihood ratio test and ultrafast bootstrap. Abbreviations as in Fig. 1.
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
<p><span>Machine learning can be as good as maximum likelihood when reconstructing phylogenetic topologies and determining the best evolutionary model on four taxon alignments.</span></p> <p><span>Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. Here we show that for quartet trees, Machine Learning using neural networks can be as good as the Maximum Likelihood method to infer the best tree topology and the best model of sequence evolution for nucleotide as well as amino acid sequences. For this purpose we simulated data sets for a wide range of branch lengths, evolutionary models and model parameters and compared the topologies and inferred models obtained with Machine learning with those obtained with the Maximum Likelihood and the Neighbour Joining method. Our results show that neural networks are a promising avenue for determining relatedness between taxa, which is likely to accelerate the construction of phylogenetic trees in the future, while maintaining a high accuracy.</span></p>
CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models
<p>Simulated datasets used in our paper "CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models" to produce figures 1bc, 1d, and 2ab. The data provided in each folder is as follows:</p> <ul> <li><strong>rate_matrices</strong> contains the classical LG rate matrix, and our 400 x 400 estimated co-evolutionary model Q2.</li> <li><strong>fig_1bc</strong> contains the simulated data used to estimate and evaluate rate matrices using the CherryML method and EM (with XRATE) as shown in Fig. 1b and c of our paper. The files and sub-directories here are: <ul> <li><strong>fig_1bc_simulated_data_families_all.txt</strong> contains the list of protein family names used to train the model. When only K families are used in Fig. 1b and c, these are the first K families of this list.</li> <li><strong>gt_tree_dir</strong> contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li><strong>msa_dir</strong> contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each tree, without site rate variation.</li> <li><strong>gt_site_rates_dir</strong> contains the site rates used. In this case, they are all 1.</li> <li><strong>gt_likelihood_dir</strong> contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> </ul> </li> <li><strong>fig_1d</strong> folder contains the simulated data used to evaluate the effect of time quantization on the CherryML method as shown in Fig. 1d of our paper. The files and sub-directories here are: <ul> <li><strong>gt_tree_dir</strong> contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li><strong>msa_dir</strong> contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each tree, with site rate variation.</li> <li><strong>gt_site_rates_dir</strong> contains the site rates used.</li> <li><strong>gt_likelihood_dir</strong> contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> </ul> </li> <li><strong>fig_2ab</strong> contains the simulated data used to evaluate the effect of time quantization on the CherryML method as shown in Fig. 1d of our paper. The files and sub-directories here are: <ul> <li><strong>gt_tree_dir</strong> contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li><strong>msa_dir</strong> contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each non-contacting tree, and using Q2 for the contacting sites, all without site rate variation.</li> <li><strong>gt_site_rates_dir</strong> contains the site rates used, in this case all 1 (i.e. no site rate variation).</li> <li><strong>gt_likelihood_dir</strong> contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> <li><strong>contact_map_dir</strong> contains the simulated contact maps for each family. These were obtained by computing a maximal matching on the true contact maps derived from the trRosetta paper, as described in detail in out paper.</li> </ul> </li> </ul> <p>The exact end-to-end code which generates these simulated datasets is provided in our Github repository: <a href="https://github.com/songlab-cal/CherryML">https://github.com/songlab-cal/CherryML</a></p> <p>In fact, by default, when you try to reproduce the figures in our paper by running the `reproduce_all_figures.py` script in our repository, the data will automatically be simulated for you if it isn't already present. This can be bypassed by downloading the data here in Zenodo and changing the top of `reproduce_all_figures.py` to point to these files.</p>
CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples
<p>This folder includes simulated and real data used in validating CNETML, a new maximum likelihood method designed to reconstruct the evolutionary history of multiple samples of a single patient which may be taken at different locations and/or times, which can take as input (relative) total integer copy numbers called from shallow whole genome sequencing data.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.