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33 results for “partition model”
Figure 4 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 4. Selected trees, with node supports (Poisson boostrap and posterior probabilities), depicting the overall variation in topologies obtained. A, parsimony IW100. B, parsimony IW5. C, Bayesian UN_p. D, Bayesian IW100_e. All topologies and branch lengths for Bayesian trees are available in the Supporting Information (File S9).
Figure 3. A in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 3. A, marginal likelihoods of Bayesian models. B, normalized Robinson–Foulds (nRF) distances among topologies (with IW100_e used as reference). C, distribution of node supports, with posterior probabilities for Bayesian inferences and bootstrap values for maximum parsimony.
Figure 7 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 7. Stratigraphic fit of maximum parsimony and Bayesian topologies evaluated with two metrics, considering fossil age intervals as known ranges or as stratigraphic uncertainty. A, stratigraphic consistency index (SCI). B, gap excess ratio (GER).
Figure 10 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 10. Relative rates (median and 95% HPD) of speciation, extinction and fossilization obtained with a skyline fossilized birth-death process for seven consecutive time bins.
Data from: Life table invasion models: spatial progression and species-specific partitioning
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Data from: PartitionFinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses.
In phylogenetic analyses of molecular sequence data, partitioning involves estimating independent models of molecular evolution for different sets of sites in a sequence alignment. Choosing an appropriate partitioning scheme is an important step in most analyses because it can affect the accuracy of phylogenetic reconstruction. Despite this, partitioning schemes are often chosen without explicit statistical justification. Here, we describe two new objective methods for the combined selection of best-fit partitioning schemes and nucleotide substitution models. These methods allow millions of partitioning schemes to be compared in realistic timeframes, and so permit the objective selection of partitioning schemes even for large multi-locus DNA datasets. We demonstrate that these methods significantly outperform previous approaches, including the ad hoc selection of partitioning schemes (e.g. partitioning by gene or codon position), and a recently proposed hierarchical clustering method. We have implemented these methods in an open-source program, PartitionFinder. This program allows users to select partitioning schemes and substitution models using a range of information-theoretic metrics (e.g. the BIC, AIC, and AICc). We hope that PartitionFinder will encourage the objective selection of partitioning schemes, and thus lead to improvements in phylogenetic analyses. PartitionFinder is written in Python and runs under Mac OSX 10.4 and above. The program, source code, and a detailed manual are freely available from .
Data from: The relative importance of modeling site pattern heterogeneity versus partition-wise heterotachy in phylogenomic inference
Large taxa-rich genome-scale data sets are often necessary for resolving ancient phylogenetic relationships. But accurate phylogenetic inference requires that they are analyzed with realistic models that account for the heterogeneity in substitution patterns amongst the sites, genes and lineages. Two kinds of adjustments are frequently used: models that account for heterogeneity in amino acid frequencies at sites in proteins, and partitioned models that accommodate the heterogeneity in rates (branch lengths) among different proteins in different lineages (protein-wise heterotachy). Although partitioned and site-heterogeneous models are both widely used in isolation, their relative importance to the inference of correct phylogenies has not been carefully evaluated. We conducted several empirical analyses and a large set of simulations to compare the relative performances of partitioned models, site-heterogeneous models and combined partitioned site heterogeneous models. In general, site-homogeneous models (partitioned or not) performed worse than site heterogeneous, except in simulations with extreme protein-wise heterotachy. Furthermore, simulations using empirically-derived realistic parameter settings showed a marked long-branch attraction (LBA) problem for analyses employing protein-wise partitioning even when the generating model included partitioning. This LBA problem results from a small sample bias compounded over many single protein alignments. In some cases, this problem was ameliorated by clustering similarly-evolving proteins together into larger partitions using the PartitionFinder method. Similar results were obtained under simulations with larger numbers of taxa or heterogeneity in simulating topologies over genes. For an empirical Microsporidia test data set, all but one tested site-heterogeneous models (with or without partitioning) obtain the correct Microsporidia+Fungi grouping, whereas site-homogenous models (with or without partitioning) did not. The single exception was the fully partitioned site-heterogeneous analysis that succumbed to the compounded small sample LBA bias. In general unless protein-wise heterotachy effects are extreme, it is more important to model site-heterogeneity than protein-wise heterotachy in phylogenomic analyses. Complete protein-wise partitioning should be avoided as it can lead to a serious LBA bias. In cases of extreme protein-wise heterotachy, approaches that cluster similarly-evolving proteins together and coupled with site-heterogeneous models work well for phylogenetic estimation.
Figure 6 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 6. Estimated rate multipliers for homoplasy-based partitions in each model.
Data from: Testing for biases in selection on avian reproductive traits and partitioning direct and indirect selection using quantitative genetic models
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Data from: The relative importance of modeling site pattern heterogeneity versus partition-wise heterotachy in phylogenomic inference
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Data from: PartitionFinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses.
Open the record for dataset details and reuse information.
Figure 9 in Reassessing the phylogeny and divergence times of sloths (Mammalia: Pilosa: Folivora), exploring alternative morphological partitioning and dating models
Figure 9. Bayesian chronogram for the best-fitting dating model (FBD_TK02_TIP). Shaded bars depict uncertainty in estimates of node and tip ages. Main sloth clades are named on the right.
Dataset related to article "Recursive partitioning model-based analysis for survival of colorectal cancer patients with lung and liver oligometastases treated with stereotactic body radiation therapy"
<p>This record contains raw data related to article “Recursive partitioning model-based analysis for survival of colorectal cancer patients with lung and liver oligometastases treated with stereotactic body radiation therapy"</p> <p><strong>Introduction: </strong>Liver and lung are common sites of metastases from colorectal cancer (CRC). Stereotactic body radiation therapy (SBRT) represents a valid treatment, with high rates of local control (LC). In this study, we applied recursive partitioning model-based analysis (RPA) to define class risks for overall survival (OS) and progression free survival (PFS) in oligometastatic CRC patients.</p> <p><strong>Materials and methods: </strong>In this monocentric analysis, we included patients with lung or liver metastases. Patients were candidate to SBRT if a maximum of 5 metastases. End points of the present analysis were LC, PFS, and OS. The binary classification tree approach with RPA was applied to stratify the patients into risk groups based on OS and PFS.</p> <p><strong>Results: </strong>218 patients were treated with SBRT on 371 metastases. Majority of patients (56%) was treated on single lesion, followed by 2 (26.1%) and 3 lesions (14.7%). Median follow-up was 22.7 months. Rates of LC were 84.2% at 1 year and 73.8% at 3 years. Rates of PFS at 1 and 3 years were 42.2% and 14.9%, respectively. RPA identified 3 classes for PFS, according to age and number of metastases with 3-year PFS of 30.6%, 13.5% and 8.4%. Overall survival was 87.2% at 1 year, 51.9% at 3 years, and 36.8% at 5 years. RPA identified 3 nodes. Class 1 included patients with liver metastases (3-year OS 35.2%). Class 2 included patients with lung metastases and DFI ≤ 48 months (3-year OS 65%). Class 3 included patients with lung metastases and DFI > 48 months (3-year OS 73.5%).</p> <p><strong>Conclusions: </strong>Stereotactic body radiation therapy can be considered an effective treatment for the management of liver and lung metastases from CRC. With RPA, we identified prognostic risk class to define patients who could benefit the most from SBRT.</p>
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