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1,066 results for “bayesian”
FIGURE 2 in Bayesian inference reveals a complex evolutionary history of belemnites
FIGURE 2. Maximum clade credibility tree of the Bayesian tip-dated analysis. Numbers at nodes represent posterior probability, while the blue bars indicate the 95% highest posterior density interval of the divergence time estimates. The small black dot represents the constrained clade. Tips with zero-length branches represent sampled ancestors.
Electronic Structure Data for "Design of Covalent Organic Frameworks through on-the-fly Batch-based Bayesian Optimization"
<p>This is a dataset of 1736 potential building blocks for the construction of covalent organic frameworks (COF). Electronic structures were calculated with the GFN1-xTB tight binding DFT approach as implemented in the xTB package (v6.2.3). The dataset contains all necessary inputs and outputs from these calculations. Structures were optimised with xTB's internal normal coordinate rational function optimizer (ANCopt) at the default geometry convergence criterion.</p> <p>The dataset contains calculations for two major parameters determining the suitability of the resulting COFs as an organic semiconductor, specifically, the approximate energy alignment of the homo level and the reorganization free energy.</p>
Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI
<p>Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets–with many taxa and/or many sites in the sequence alignment–or complex substitution models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware-specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. Additionally, we implemented a native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and a powerful NVidia Titan V GPU against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used with up to 20-fold speedup when using multiple CPUs and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent rescaling can increase runtimes 2.5 to 3-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes which can speed up computations but comes at twice the memory requirements.</p> <p>Availability: The software described in the paper is available at https://github.com/revbayes/revbayes with documentation and tutorials found at https://revbayes.github.io.</p>
Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes
<p>Yield and Conversion measurements for iodoalkylation reaction of four different terminal alkynes. The reaction conditions as well as the equivalent of the reactants and reagents for each of the three optimizers are listed in the corresponding JSON file. </p>
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West) in Bumps on the back: An unusual morphology in phylogenetically distinct Peridinium aff. cinctum (= Peridinium tuberosum; Peridiniales, Dinophyceae)
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West)
◂Fig. 4 A molecular tree of 51 systematically representative Peridiniaceae, including all 28 accessions assignable to P. volzii. Maximum Likelihood tree (–ln = 22,017.62), as inferred from a rRNA nucleotide alignment (1,129 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (abbreviations: HET, Heterocapsaceae; PPE, Protoperidiniaceae) in Morphological and molecular variability of Peridinium volzii Lemmerm. (Peridiniaceae, Dinophyceae) and its relevance for infraspecific taxonomy
◂Fig. 4 A molecular tree of 51 systematically representative Peridiniaceae, including all 28 accessions assignable to P. volzii. Maximum Likelihood tree (–ln = 22,017.62), as inferred from a rRNA nucleotide alignment (1,129 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (abbreviations: HET, Heterocapsaceae; PPE, Protoperidiniaceae)
Fig. 1. Bayesian phylogenetic tree constructed using partial cytochrome b in Unexpected absence of exo-erythrocytic merogony during high gametocytaemia in two species of Haemoproteus (Haemosporida: Haemoproteidae), including description of Haemoproteus angustus n. sp. (lineage hCWT7) and a report of previously unknown residual bodies during in vitro gametogenesis
Fig. 1. Bayesian phylogenetic tree constructed using partial cytochrome b sequences of 61 lineages of Haemoproteus, 4 lineages of Plasmodium, and Leucocytozoon sp. lSISKIN2 as outgroup. Posterior probabilities higher than 0.8 are indicated close to the respective nodes. Red font indicates the parasite lineage described in this publication. Vertical bars (A–D) show groups of closely related lineages, which complete development and produce gametocytes only in non-passerines (A, D), both non-passerines and passerines (B), and only passerines (C). Blue font indicates Haemoproteus species, which develop in non-passerine avian hosts, which are indicated by symbols (● – Psittaciformes; ∎ - Coraciiformes; ▴ - Strigiformes; ◆ - Anseriformes; ★ - Charadriiformes; ♥ - Pelecaniformes; ⋄ - Piciformes; ⊠ - Sphenisciformes; Ω - Musophagiformes; § - Trochiliformes; Ψ – Falconiformes; Σ – Columbiformes; Φ - Galliformes). Lineage names were provided (according to MalAvi database), followed by parasite species names and sequence GenBank accession numbers.
Fig. 1. The modified time calibrated Bayesian tree and a in Fig. 3 in Fig. 4 in Fig. 4 in Responses of Phyllostomid Bats to Traditional Agriculture in Neotropical Montane Forests of Southern Mexico.
Fig. 1. The modified time calibrated Bayesian tree and a plot of four major avian developmental modes (Prum et al. 2015). The complete tree is divided into parts A and B. Scale in the Y-axis: millions of years ago.
Fig. 1. The modified time calibrated Bayesian tree and a in Fig. 3 in Fig. 4 in Fig. 4 in Responses of Phyllostomid Bats to Traditional Agriculture in Neotropical Montane Forests of Southern Mexico.
Fig. 1. The modified time calibrated Bayesian tree and a plot of four major avian developmental modes (Prum et al. 2015). The complete tree is divided into parts A and B. Scale in the Y-axis: millions of years ago.
Fig. 1. Circular Bayesian tree inferred from mtDNA cox-2 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 1. Circular Bayesian tree inferred from mtDNA cox-2 sequences obtained from specimens of C. osculatum sp. D and C. osculatum sp. E analysed in the present study, based on Bayesian Inference (BI) method using MrBayes v3.2.2 (Ronquist et al., 2012). Evolutionary distance was estimated using the TrN + G (G = 0.60) substitution model as implemented in jModeltest (Posada, 2008), with the AIC approach (Posada and Buckley, 2004). Posterior probability values are the result of 1.000000 of runs and are reported at the nodes. The coloured icons correspond to the two species considered in this study (red = C. osculatum sp. D and blue = C. osculatum sp. E).
Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies
<p>This repository contains the nexus files and MrBayes command files needed for running the experiments to determine the optimal word list size required for inferring the best phylogenies.</p> <p>The paper is forthcoming at <strong>The 27th International Conference on Computational Linguistics (COLING 2018), Santa Fe, New-Mexico, USA</strong>.</p>
Data for "BayesCMD: A Bayesian framework for the analysis of systems biology models of the brain"
<p>Data for the "BayesCMD: A Bayesian framework for the analysis of systems biology models of the brain".</p> <p>All files except 'simulated_hypoxia.csv' contains both input and output data.</p>
Bayesian Time-Resolved Spectra of GRB Pulses
<p>Spectral analysis results of 38 gamma-ray burst pulses for two empirical models: the cutoff powerlaw (CPL) and the Band function (BAND). Standard FITS file format. Bayesian inference done by the astrophysical data analysis software 3ML.</p>
Bayesian Methodology: An overview with the help of R software
<p>Bayesian methodology differs from traditional statistical methodology which involves frequentist approach. Bayesian methodology was introduced by Thomas Bayes (Statistician and minister at the Presbyterian Chapel) during the 18<sup>th</sup> Century. Bayesian methodology is now widely being used due to its simple, straightforward and interpretable characteristics of probability values and the efficiency of modern day computer systems.</p> <p>Bayesian methodology is now being used in the field of clinical research, clinical trials, epidemiology, econometrics, statistical process control, marketing research and statistical mechanics. It also used in the emerging field such as data science (machine learning and deep learning) and big data analytics.</p> <p>The book provides an overview of Bayesian methodology, its uses in different fields with the help of R statistical open source software.</p> <p><a href="https://www.amazon.com/dp/B07QCHTR54">https://www.amazon.com/dp/B07QCHTR54</a></p> <p><strong>ISBN-13: 978-1092939898</strong></p> <p> </p> <p><strong>Editor</strong></p> <p><strong>International Journal of Statistics and Medical Informatics</strong></p> <p><a href="http://www.ijsmi.com/book.php"><strong>www.ijsmi.com/book.php</strong></a></p>
Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis
<p>This dataset contains the results of 2 related analyses, described in "Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis" (European Heart Journal Supplements, OUP). Link to the article: https://www.hal.inserm.fr/inserm-02310241</p> <p>1) In the directory "miRNAs_MARTHA_GWAS" : GWAS summary statistics for 162 circulating miRNAs in 344 VTE patients from the MARTHA cohort.</p> <p>Header for each summary file:</p> <p>Trait: miRNA id<br> chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> freq_A1: Frequency of reference allele<br> rsqr: Imputation quality defined by MACH<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)<br> z.score: Z-score</p> <p> </p> <p>2) In the directory "meta_analysis": Random effect meta-analysis combining the results of our GWAS on the MARTHA cohort, and the results from a similar analysis conducted by Nikpay et al. (doi: 10.1093/cvr/cvz030). Summary statistics of 142 microRNAs, common to both datasets, were processed (and combine 1054 samples).</p> <p>Header for each summary file:</p> <p>chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> N: Sample size<br> Q: Cochran's heterogeneity statistic<br> Q.p: p-value of Cochran's Q<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)</p> <p> </p>
Figure 4. Phylogeny constructed through Bayesian inference estimated from the 35H in Gene Flow Patterns of the Aedes aegypti (Diptera: Culicidae) Mosquito in Colombia: a Continental Comparison Suggests Multiple Invasion Routes and Gene Exchange
Figure 4. Phylogeny constructed through Bayesian inference estimated from the 35H found of the ND4 gene for the A. aegypti populations in the American continent. The blue horizontal bars above the branches reflect the 95% CI for the branch supports. The color bars (blue, green, and red) on the tree terminals indicate which haplotypes are exclusive for a specific population. The dotted lines on the right side of the tree and numbers I or II indicate to what clade each of the terminals belong. H1-Col (Colombia (Sucre and Quindio), Venezuela, Peru, M-NA, Brasil (MA-O, RBPV, SEBr, BE-L)), H4 (Venezuela, M-NA, Brasil (MA-O, RBPV, BEL)), H3 (Venezuela, M-NA, Brazil (RBPV, SEBr, BE-L)), H2-Col (Colombia (Sucre), Venezuela, Peru, M-NA, Brazil (RBPV, SEBr, BE-L)), H13 (M-NA, Brazil (SEBr)), H8 (Venezuela, Brazil (SEBr)).
Figure 4. Bayesian inference tree for 8103 in A new species of Orobdella (Hirudinida, Arhynchobdellida, Orobdellidae) from the Tsukuba Mountains in Japan
Figure 4. Bayesian inference tree for 8103 bp of nuclear 18S rRNA, 28S rRNA and H3, and mitochondrial COI, tRNACys, tRNAMet, 12S rRNA, tRNAVal, 16S rRNA, tRNALeu and ND1 markers. Numbers on nodes indicate bootstrap (BS) values for maximum likelihood ≥ 50 % and Bayesian posterior probabilities (PP) ≥ 0.90. An asterisk denotes the node with BS = 100 % and PP ≥ 1.0.
Fig. 2. Bayesian Inference tree constructed from Internal transcribed Spacer 2 in Ecological and geographical speciation in Lucilia bufonivora: The evolution of amphibian obligate parasitism
Fig. 2. Bayesian Inference tree constructed from Internal transcribed Spacer 2 (non-coding) sequence data. Each specimen is labelled with the species name and location abbreviation as indicated in Table 1. Green text corresponds to European samples of Lucilia bufonivora; red represents Lucilia elongata; purple represents Canadian L. bufonivora; orange represents Lucilia silvarum. Scale bar represents expected changes per site. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2. Bayesian tree reconstructed using 478 in Detection of haemosporidian parasites in wild and domestic birds in northern and central provinces of Iran: Introduction of new lineages and hosts
Fig. 2. Bayesian tree reconstructed using 478-bp mitochondrial cytb gene for avian blood parasites lineages. The amplified sequences in the current study are highlighted in bold. Posterior probability support of>0.8 is displayed for each branch. Schematic tree is summarized in section A and separated clade for each genus is given in sections of B (Plasmodium), C (Haemoproteus), and D (Leucocytozoon).
Fig. 3. Bayesian phylogenetic tree using the 12S in Characterization of aortic and brachiocephalic filariasis by Filarioidea sp (Nematoda:Spirurida:Filarioidea) in Mexican ramphastids
Fig. 3. Bayesian phylogenetic tree using the 12S mitochondrial sequences for different species of filariae. The number of the nodes indicate the values of support or posterior probability.
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.