Maximum Likelihood Inference of Phylogenetic Trees, with Special Reference to a Poisson Process Model of DNA Substitution and to Parsimony Analyses

1990 ◽  
Vol 39 (4) ◽  
pp. 345 ◽  
Author(s):  
Nick Goldman
Keyword(s):  
Maximum Likelihood ◽  
Poisson Process ◽  
Special Reference ◽  
Phylogenetic Trees ◽  
Process Model ◽  
Likelihood Inference ◽  
Dna Substitution

The identification of a novel HIV-1 second-generation recombinant form (CRF01_AE/CRF07_BC) among men who have sex with men in Jiangsu, China

2020 ◽  
Vol 18 ◽  
Author(s):  
Yin Yueqi ◽  
Zhou Ying ◽  
Lu Jing ◽  
Guo Hongxiong ◽  
Chen Jianshuang ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Trees ◽  
Second Generation ◽  
Emergency Situation ◽  
The World ◽  
Break Points ◽  
Sex With Men ◽  
Full Length Genome ◽  
Virus Strains ◽  
Hiv 1

Background: CRF01_AE and CRF07_BC are the two major HIV-1 virus strains circulating in China. The proportion of dominant subtypes (CRF01_AE and CRF07_BC) among MSM in Jiangsu province was over 80%. A large number of URFs have been found in China in recently years. Objective: This study aimed to report on novel HIV-1 recombinants. Method: We constructed Phylogenetic trees using the maximum likelihood (ML) method with 1000 bootstrap replicates in IQ-TREE 1.6.8 software and determined recombination break points using SimPlot 3.5.1. Results: We identified a novel, second-generation HIV-1 recombinant (JS020202) between CRF01_AE and CRF07_BC. The analysis of near full-length genome (NFLG) showed there were at least 8 breakpoints inner virus, which differed from any previously identified CRF and URF around the world. Conclusion: Novel diverse CRF01_AE/07_BC suggested the complexity trends of HIV-1 genetics. The emergency situation of diverse recombinant strains should be monitored continuously.

scholarly journals Maximum likelihood inference of reticulate evolutionary histories

2014 ◽  
Vol 111 (46) ◽  
pp. 16448-16453 ◽  
Author(s):  
Yun Yu ◽  
Jianrong Dong ◽  
Kevin J. Liu ◽  
Luay Nakhleh
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Inference

scholarly journals Maximum Likelihood Inference for Univariate Delay Differential Equation Models with Multiple Delays

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Ahmed A. Mahmoud ◽  
Sarat C. Dass ◽  
Mohana S. Muthuvalu ◽  
Vijanth S. Asirvadam
Keyword(s):  
Differential Equation ◽  
Maximum Likelihood ◽  
Delay Differential Equation ◽  
Information Matrix ◽  
Likelihood Inference ◽  
Multiple Delays ◽  
Unknown Parameters ◽  
Differential Equation Models ◽  
Delay Differential ◽  
Initial Starting

This article presents statistical inference methodology based on maximum likelihoods for delay differential equation models in the univariate setting. Maximum likelihood inference is obtained for single and multiple unknown delay parameters as well as other parameters of interest that govern the trajectories of the delay differential equation models. The maximum likelihood estimator is obtained based on adaptive grid and Newton-Raphson algorithms. Our methodology estimates correctly the delay parameters as well as other unknown parameters (such as the initial starting values) of the dynamical system based on simulation data. We also develop methodology to compute the information matrix and confidence intervals for all unknown parameters based on the likelihood inferential framework. We present three illustrative examples related to biological systems. The computations have been carried out with help of mathematical software: MATLAB® 8.0 R2014b.

scholarly journals Bayesian modeling and prediction of accrual in multi-regional clinical trials

2014 ◽  
Vol 26 (2) ◽  
pp. 752-765 ◽  
Author(s):  
Yi Deng ◽  
Xiaoxi Zhang ◽  
Qi Long
Keyword(s):  
Clinical Trials ◽  
Poisson Process ◽  
Process Model ◽  
Nonhomogeneous Poisson Process ◽  
Modeling And Prediction ◽  
Accuracy And Precision ◽  
Start Up ◽  
Accrual Rate ◽  
Model Region ◽  
Over Time

In multi-regional trials, the underlying overall and region-specific accrual rates often do not hold constant over time and different regions could have different start-up times, which combined with initial jump in accrual within each region often leads to a discontinuous overall accrual rate, and these issues associated with multi-regional trials have not been adequately investigated. In this paper, we clarify the implication of the multi-regional nature on modeling and prediction of accrual in clinical trials and investigate a Bayesian approach for accrual modeling and prediction, which models region-specific accrual using a nonhomogeneous Poisson process and allows the underlying Poisson rate in each region to vary over time. The proposed approach can accommodate staggered start-up times and different initial accrual rates across regions/centers. Our numerical studies show that the proposed method improves accuracy and precision of accrual prediction compared to existing methods including the nonhomogeneous Poisson process model that does not model region-specific accrual.

scholarly journals Evolutionary relationships of completely sequenced Clostridia species and close relatives

2015 ◽  
Vol 65 (Pt_11) ◽  
pp. 4276-4283 ◽  
Author(s):  
Takashi Kunisawa
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Trees ◽  
Gene Arrangement ◽  
Evolutionary Relationships ◽  
Rrna Gene ◽  
Sequence Information ◽  
Bayesian Tree ◽  
Maximum Likelihood Methods ◽  
Genome Sequence Information ◽  
Close Relatives

The class Clostridia in the phylum Firmicutes includes a very heterogeneous assemblage of bacteria. Their evolutionary relationships are not well established; revisions of their phylogenetic placements based on comparative studies of 16S rRNA gene sequences are in progress as genome sequence information accumulates. In this work, phylogenetic trees were reconstructed based on 21 concatenated ribosomal protein sequences using Bayesian and maximum-likelihood methods. Both trees consistently indicate that the Halanaerobiales is a deeply branching order among the class Clostridia. The rest of the Clostridia species are grouped into 10 monophyletic clusters, most of which are comprised of two or three orders and families according to the current Clostridial taxonomy. The maximum-likelihood tree placed Coprothermobacter proteolyticus and Thermodesulfobium narugense in the class Clostridia in accordance with the current taxonomy, in which these two bacteria are assigned to the family Thermodesulfobiaceae. However, the Bayesian tree placed these two bacteria at the boundary between the Firmicutes and Actinobacteria. A gene arrangement that is present uniquely in the Firmicutes species was identified. Both Coprothermobacter proteolyticus and Thermodesulfobium narugense do not have this arrangement characteristic of the Firmicutes. On the basis of the Bayesian tree and gene arrangement comparison, it is suggested that Coprothermobacter proteolyticus and Thermodesulfobium narugense should be placed outside the phylum Firmicutes.

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