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Dataset results
19 results for “Maximum likelihood estimation”
Figure. Phylogram showing phylogenetic relationships estimated using maximum likelihood analysis of 16S rRNA and COXI gene revealed the grouping of Orthochirus iranus, O. farzanpay, O. stockwelli, O. zagrosensis, O. innesi (JQ514244.1 Morocco), and O. bicolor (KT716038.1 India), with the outgroup species Androctonus crassicauda (FJ217732). in A study of genetic diversity among different population of Orthochirus sp. based on cytochrome C oxidase subunit I and 16srRNA sequencing
Figure. Phylogram showing phylogenetic relationships estimated using maximum likelihood analysis of 16S rRNA and COXI gene revealed the grouping of Orthochirus iranus, O. farzanpay, O. stockwelli, O. zagrosensis, O. innesi (JQ514244.1 Morocco), and O. bicolor (KT716038.1 India), with the outgroup species Androctonus crassicauda (FJ217732).
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.
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>
Fig. 3. Maximum likelihood tree estimated from the 215 in Morphological and Molecular Identification of Isospora sepetibensis (Chromista: Miozoa: Eimeriidae) from a New Host, Trichothraupis melanops (Passeriformes: Thraupidae: Tachyphoninae) in South America
Fig. 3. Maximum likelihood tree estimated from the 215 bp long cox1 sequences. Numbers at nodes represent bootstrap support (1,000 replicates; only values> 50% shown) for Neighbor-Joining and Maximum Likelihood, respectively. The scale-bar represents the number of nucleotide substitutions per site.
Fig. 2. Maximum likelihood tree estimated from the cox1 in Morphological and Molecular Identification of Isospora sepetibensis (Chromista: Miozoa: Eimeriidae) from a New Host, Trichothraupis melanops (Passeriformes: Thraupidae: Tachyphoninae) in South America
Fig. 2. Maximum likelihood tree estimated from the cox1 sequences. Numbers at nodes represent bootstrap support (1,000 replicates; only values> 50% shown) for Neighbor-Joining and Maximum Likelihood, respectively. The scale-bar represents the number of nucleotide substitutions per site.
Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
<p>The data part of this release support the results presented in the paper <br /> "Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data", by W. Wang and G. Casale, accepted for presentation at MAMA workshop 2015. </p> <p>When referring to the dataset or scripts please cite the paper above. </p>
Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
<p>The data part of this release support the results presented in the paper <br /> "Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data", by W. Wang, G. Casale, A. Kattepur and M. Nambiar, accepted for presentation at ICPE 2016. </p> <p>When referring to the dataset or scripts please cite the paper above. </p>
FIGURE 18. Maximum Likelihood tree estimated from 1044 in Bythaelurus bachi n. sp., a new deep-water catshark (Carcharhiniformes, Scyliorhinidae) from the southwestern Indian Ocean, with a review of Bythaelurus species and a key to their identification
FIGURE 18. Maximum Likelihood tree estimated from 1044 aligned sites of the mitochondrial NADH2 gene using a General Time Reversible model and an accommodation for among site rate variation and Invariant sites (GTR+I+G model).
Code and data for maximum likelihood estimation (MLE) of P. falciparum age from RNA-seq data
<p>The original R code was written by Avi Feller and Jacob Lemieux and is described in this publication:</p> <p>Lemieux JE, Gomez-Escobar N, Feller A, Carret C, Amambua-Ngwa A, Pinches R, Day F, Kyes SA, Conway DJ, Holmes CC and Newbold CI (2009): Statistical estimation of cell-cycle progression and lineage commitment in <em>Plasmodium falciparum </em>reveals a homogeneous pattern of transcription in <em>ex vivo</em> culture. <em>Proc Natl Acad Sci U S A</em> 106(18), 7559-7564 doi: 10.1073/pnas.0811829106 <a href="https://www.pnas.org/doi/full/10.1073/pnas.0811829106">https://www.pnas.org/doi/full/10.1073/pnas.0811829106</a></p> <p>In this deposition, the original R script is adapted for command-line use. For usage instructions, see file "README.md".</p> <p>Furthermore, this deposition contains RNA-seq data for <em>P. falciparum</em> 3D7 cultured with blood from individuals with high, normal or low iron status (experiment 1) or with blood from a healthy donor in the presence vs. absence of 0.7 µM hepcidin (experiment 2) at 6 – 9 and 26 – 29 hours post erythrocyte invasion (hpi). For more details see publication:</p> <p>Iron transport pathways in the human malaria parasite <em>Plasmodium falciparum</em> revealed by RNA-sequencing, Wunderlich et al. (<a href="https://www.biorxiv.org/content/10.1101/2024.04.18.590068v1" target="_blank" rel="noopener">biorxiv</a>).</p> <p>Finally, this deposition contains a variety of pre-processed datasets obtained from the following references:</p> <p>Bártfai R, Hoeijmakers WA, Salcedo-Amaya AM, Smits AH, Janssen-Megens E, Kaan A, Treeck M, Gilberger TW, Françoijs KJ and Stunnenberg HG (2010): H2A.Z demarcates intergenic regions of the <em>Plasmodium falciparum</em> epigenome that are dynamically marked by H3K9ac and H3K4me3. <em>PLoS Pathog</em> 6(12), e1001223 doi: 10.1371/journal.ppat.1001223</p> <p>Broadbent KM, Broadbent JC, Ribacke U, Wirth D, Rinn JL and Sabeti PC (2015): Strand-specific RNA sequencing in <em>Plasmodium falciparum</em> malaria identifies developmentally regulated long non-coding RNA and circular RNA. <em>BMC Genomics</em> 16(1), 454 doi: 10.1186/s12864-015-1603-4</p> <p>López-Barragán MJ, Lemieux J, Quiñones M, Williamson KC, Molina-Cruz A, Cui K, Barillas-Mury C, Zhao K and Su X-z (2011): Directional gene expression and antisense transcripts in sexual and asexual stages of <em>Plasmodium falciparum</em>. <em>BMC Genomics</em> 12(1), 587 doi: 10.1186/1471-2164-12-587</p> <p>Otto TD, Wilinski D, Assefa S, Keane TM, Sarry LR, Böhme U, Lemieux J, Barrell B, Pain A, Berriman M, Newbold C and Llinás M (2010): New insights into the blood-stage transcriptome of <em>Plasmodium falciparum</em> using RNA-Seq. <em>Mol Microbiol</em> 76(1), 12-24 doi: 10.1111/j.1365-2958.2009.07026.x</p> <p>Siegel TN, Hon CC, Zhang Q, Lopez-Rubio JJ, Scheidig-Benatar C, Martins RM, Sismeiro O, Coppée JY and Scherf A (2014): Strand-specific RNA-Seq reveals widespread and developmentally regulated transcription of natural antisense transcripts in <em>Plasmodium falciparum</em>. <em>BMC Genomics</em> 15(1), 150 doi: 10.1186/1471-2164-15-150</p> <p>Wichers JS, Scholz JAM, Strauss J, Witt S, Lill A, Ehnold LI, Neupert N, Liffner B, Lühken R, Petter M, Lorenzen S, Wilson DW, Löw C, Lavazec C, Bruchhaus I, Tannich E, Gilberger TW and Bachmann A (2019): Dissecting the gene expression, localization, membrane topology, and function of the <em>Plasmodium falciparum</em> STEVOR protein family. <em>mBio</em> 10(4), doi: 10.1128/mBio.01500-19</p>
FIGURE. Maximum likelihood phylogram of Multiclavula spp. based on ITS sequences. Rooted to Clavulina cristata. Bar = estimated changes/nucleotide. Support values above or below branches: Bayesian posterior probability/maximum likelihood bootstrap. in New and interesting species of Agaricomycetes from Panama
FIGURE. Maximum likelihood phylogram of Multiclavula spp. based on ITS sequences. Rooted to Clavulina cristata. Bar = estimated changes/nucleotide. Support values above or below branches: Bayesian posterior probability/maximum likelihood bootstrap.
FIGURE. Maximum likelihood phylogram of Humidicutis spp. based on ITS sequences. Rooted to Humidicutis marginata. Bar = estimated changes/nucleotide. Support values above or below branches: Bayesian posterior probability/maximum likelihood bootstrap. in New and interesting species of Agaricomycetes from Panama
FIGURE. Maximum likelihood phylogram of Humidicutis spp. based on ITS sequences. Rooted to Humidicutis marginata. Bar = estimated changes/nucleotide. Support values above or below branches: Bayesian posterior probability/maximum likelihood bootstrap.
FIGURE 2. Maximum likelihood tree estimated from the COI1 in Molecular identification of Isospora coerebae Berto, Flausino, Luz, Ferreira & Lopes, 2010 (Chromista: Miozoa: Eimeriidae) from the bananaquit Coereba flaveola (Linnaeus, 1758) (Passeriformes: Thraupidae: Coerebinae) from Brazil
FIGURE 2. Maximum likelihood tree estimated from the COI1 gene sequences of coccidian species. Numbers at the nodes show posterior probabilities under the Bayesian Inference analysis/bootstrap values derived from Maximum Likelihood analysis. Scale bar represents the number of nucleotide substitutions per site.
FIGURE 3. Maximum likelihood tree estimated from the COI2 in Molecular identification of Isospora coerebae Berto, Flausino, Luz, Ferreira & Lopes, 2010 (Chromista: Miozoa: Eimeriidae) from the bananaquit Coereba flaveola (Linnaeus, 1758) (Passeriformes: Thraupidae: Coerebinae) from Brazil
FIGURE 3. Maximum likelihood tree estimated from the COI2 gene sequences of coccidian species. Numbers at the nodes show posterior probabilities under the Bayesian Inference analysis/bootstrap values derived from Maximum Likelihood analysis. Scale bar represents the number of nucleotide substitutions per site.
Fig. 3. Maximum likelihood gene trees estimated using PhyML. A in Morphological and Genetic Characterization of the First Species of Thalassodrilides (Annelida: Clitellata: Naididae: Limnodriloidinae) from Japan
Fig. 3. Maximum likelihood gene trees estimated using PhyML. A, COI; B, ITS. Numbers at branches denote aLRT branch support. Scale shows estimated numbers of nucleotide substitutions per site.
Data from: Object recognition and localization from 3D point clouds by maximum-likelihood estimation
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Data from: Estimating the effect of competition on trait evolution using maximum likelihood inference
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Data from: Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood
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Maximum Likelihood Estimate of Payoffs from Time Series
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Nimbus-7 Narrow Field of View (NFOV) Maximum Likelihood Cloud Estimation (MLCE) Data in Native Format
NIMBUS7_NFOV_MLCE data are Nimbus 7 Narrow Field of View (NFOV) Maximum Likelihood Cloud Estimation (MLCE) Data in Native Format.The NIMBUS7_NFOV_MLCE data set uses the Nimbus-7 measurements and the MLCE algorithm for better regional and temporal resolution. The Earth Radiation Budget (ERB) parameters, derived from the Nimbus-7 scanner measurements, were rederived in 1990 using a Maximum Likelihood Cloud Estimation (MLCE) algorithm similar, but not identical, to the Earth Radiation Budget Experiment (ERBE) algorithm. Daily and monthly means are presented on two commensurate equal area world grids: (167 km by 167 km) and (500 km by 500 km). The MLCE procedure also yielded a rough estimate of the regional cloud cover.The scanner took measurements from November 16, 1978 through June 20, 1980; however, only 13 months (May 1979 through May 1980) of data sampling were reprocessed using the Sorting into Angular Bins and MLCE algorithms. There was poorer temporal sampling during the first five months of the experiment.The Nimbus 7 research-and-development satellite served as a stabilized, earth-oriented platform for the testing of advanced systems for sensing and collecting data in the pollution, oceanographic and meteorological disciplines. The polar-orbiting spacecraft consisted of three major structures: (1) a hollow torus-shaped sensor mount, (2) solar paddles, and (3) a control housing unit that was connected to the sensor mount by a tripod truss structure.
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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.