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Novel Phylogenetic Network Inference by Combining Maximum Likelihood and Hidden Markov Models
[chapter]
Lecture Notes in Computer Science
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The model describes the phylogenetic network as a Hidden Markov Model (HMM), where each hidden state is related to one of the network's trees. ...
In this work we suggest a new probabilistic model for analyzing and modeling phylogenetic networks, the NET-HMM. ...
Acknowledgment T.T. was supported by the Edmond J. Safra Bioinformatics program at Tel Aviv University. ...
doi:10.1007/978-3-540-87361-7_30
fatcat:e4pd4gtfujd2ze6eigbmpcqg6e
THE NET-HMM APPROACH: PHYLOGENETIC NETWORK INFERENCE BY COMBINING MAXIMUM LIKELIHOOD AND HIDDEN MARKOV MODELS
2009
Journal of Bioinformatics and Computational Biology
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The model describes the phylogenetic network as a Hidden Markov Model (HMM), where each hidden state is related to one of the network's trees. ...
In this work we suggest a new probabilistic model, the NET-HMM, for analyzing and modeling phylogenetic networks. ...
Acknowledgment T.T. was supported by the Edmond J. Safra Bioinformatics Program at Tel Aviv University and the Yeshaya Horowitz Association through the Center for Complexity Science. ...
doi:10.1142/s021972000900428x
pmid:19634195
fatcat:ss6qxsre5bcwtbjk6t62n6vgbq
Pathway Modeling: New face of Graphical Probabilistic Analysis
2008
Journal of Proteomics & Bioinformatics
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Graphical probabilistic approaches are one of the unique methodologies that are used for designing and analyzing pathways. ...
Modeling biological pathways is interesting as well as difficult to optimize. Various modeling problems of diseases can be successfully analyzed using this simulation approach. ...
Maximum Likelihood is used in phylogenetic estimates, study genetic cross-over, pathway modeling and gene expression analysis. ...
doi:10.4172/jpb.1000035
fatcat:jcoxyyn3bfh6zhd3zhauwq6rhu
A Perspective of Conventional and Bio-inspired Optimization Techniques in Maximum Likelihood Parameter Estimation
2018
Journal of Autonomous Intelligence
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Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. ...
This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and ...
Acknowledgments This work is partly supported by the Fundamental Research Funds for the Central Universities in China (HUST: 2016YXMS105). ...
doi:10.32629/jai.v1i2.28
fatcat:7hfsl4shkjbpvggyvydwgv2lle
Efficient approximations for learning phylogenetic HMM models from data
2004
Bioinformatics
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Unfortunately, computing the likelihood of phylogenetic-HMM models is intractable. ...
In particular, we consider a general probabilistic model described in Siepel and Haussler that we call the phylogenetic-HMM model which generalizes the classical probabilistic models of Neyman and Felsenstein ...
In Section 2, we describe phylogenetic-HMM models in terms of Bayesian networks or DAG models. ...
doi:10.1093/bioinformatics/bth917
pmid:15262795
fatcat:4vxechrwlnh6pcczxdwfhomksi
Detection of Recombination in DNA Multiple Alignments with Hidden Markov Models
2001
Journal of Computational Biology
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Key words: phylogenetic trees, multiple alignments of DNA sequences, recombination, hidden Markov models, maximum likelihood and the expectation maximization (EM) algorithm. ...
DETECTION OF RECOMBINATION 403 due to recombination with a hidden Markov model (HMM). ...
ACKNOWLEDGMENTS This work was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) Bioinformatics Initiative and the Scottish Executive Rural Affairs Department (SERAD). ...
doi:10.1089/106652701752236214
pmid:11571075
fatcat:4h5i22k3nva5jc2zpsgtob4uwm
Phylogenetic systematics turns over a new leaf
2001
Trends in Ecology & Evolution
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Long restricted to the domain of molecular systematics and studies of molecular evolution, likelihood methods are now being used in analyses of discrete morphological data, specifically to estimate ancestral ...
Biologists are beginning to apply likelihood models within a Bayesian statistical framework, which promises not only to provide answers that evolutionary biologists desire, but also to make practical the ...
Lewis, Mark Pagel, Chris Simon and Ziheng Yang for their careful reading of earlier drafts of this article. I also gratefully acknowledge the Alfred P. ...
doi:10.1016/s0169-5347(00)02025-5
pmid:11146142
fatcat:nzirg2ymrvfeznnsavt7hvz7s4
Ancestral reconstruction of protein interaction networks
2019
PLoS Computational Biology
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We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification ...
molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. ...
modeling framework and the Texas Advanced Computing Center at the University of Texas for high-performance computing resources. ...
doi:10.1371/journal.pcbi.1007396
pmid:31658251
pmcid:PMC6837550
fatcat:sgmvzzebx5hhzoxjo3guontk7e
Models of coding sequence evolution
2008
Briefings in Bioinformatics
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These models have become increasingly sophisticated and combined with statistical model comparison techniques have helped to shed light on how genes and proteins evolve. ...
Probabilistic models of sequence evolution are in widespread use in phylogenetics and molecular sequence evolution. ...
The combination of hidden Markov models to model rate variation along the sequence and phylogenetic models describing sequence evolution across taxa was introduced to model autocorrelation of evolutionary ...
doi:10.1093/bib/bbn049
pmid:18971241
pmcid:PMC2638624
fatcat:pokp3ypkszfunjftblwlaoxmji
Advances in Computational Methods for Phylogenetic Networks in the Presence of Hybridization
[article]
2018
arXiv
pre-print
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Phylogenetic networks extend phylogenetic trees to allow for modeling reticulate evolutionary processes such as hybridization. ...
In particular, parsimony, likelihood, and Bayesian methods have been devised for estimating phylogenetic networks and associated parameters using estimated gene trees as data. ...
While the approaches pursued at the time were basic, they were foundational in terms of pursuing more sophisticated models and approaches by Nakhleh and his group. ...
arXiv:1808.08662v1
fatcat:par4fybfkrdu5m4bfiunqgs4xm
Likelihood-Based Inference of Phylogenetic Networks from Sequence Data by PhyloDAG
[chapter]
2015
Lecture Notes in Computer Science
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Processes such as hybridization, horizontal gene transfer, and recombination result in reticulation which can be modeled by phylogenetic networks. ...
Earlier likelihood-based methods for inferring phylogenetic networks from sequence data have been encumbered by the computational challenges related to likelihood evaluations. ...
The anonymous reviewers suggested a comparison to the PhyloNet method and made several other suggestions that significantly improved the paper. ...
doi:10.1007/978-3-319-21233-3_10
fatcat:7jas5a7flfdjvhvressyuwnswi
Ancestral Reconstruction of Protein Interaction Networks
[article]
2018
bioRxiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
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We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification ...
molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. ...
In general, fitting the model by maximum likelihood performed poorly by this measure on both training and test data (Figure 3A ). We then tried fitting the model using the APS itself as a criterion. ...
doi:10.1101/408773
fatcat:ow2ld4oty5gdrdncemjxaguom4
Stochastic models of sequence evolution including insertion—deletion events
2009
Statistical Methods in Medical Research
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Comparison of sequences that have descended from a common ancestor based on an explicit stochastic model of substitutions, insertions and deletions has risen to prominence in the last decade. ...
Besides the computational challenges, increasing realism in the underlying models is presently being addressed. ...
Acknowledgements This work was supported by BBSRC grant BB/C509566/1. I.M. was also supported by a Bolyai postdoctoral fellowship and an OTKA grant F 61730. ...
doi:10.1177/0962280208099500
pmid:19221170
fatcat:qu7i5gvy3zawjfap3kyghjarla
Hidden Markov Models for Evolution and Comparative Genomics Analysis
2013
PLoS ONE
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Citation: Bykova NA, Favorov AV, Mironov AA (2013) Hidden Markov Models for Evolution and Comparative Genomics Analysis. PLoS ONE 8(6): e65012. ...
The main idea is formulation of the problem as a hidden Markov model on a tree (tree HMM, tHMM), where the basic continuous-time Markov model is expanded with the introduction of emission probabilities ...
Acknowledgments The authors thank Mikhail Gelfand and Eugene Leushkin for fruitful discussions, and Pavel Novichkov for sharing the CRP data. ...
doi:10.1371/journal.pone.0065012
pmid:23762278
pmcid:PMC3676395
fatcat:lcohneuvavgmbafyfxfhkoo6mi
Algebraic Statistics in Practice: Applications to Networks
2017
Annual Review of Statistics and Its Application
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In this review, we illustrate this on three problems related to networks: network models for relational data, causal structure discovery, and phylogenetics. ...
For each problem, we give an overview of recent results in algebraic statistics, with emphasis on the statistical achievements made possible by these tools and their practical relevance for applications ...
Algebraic approaches to phylogenetics avoid parameter inference altogether and make phylogenetic inference feasible for the most general Markov model, the GMM. ...
doi:10.1146/annurev-statistics-031017-100053
fatcat:cmd2g7mbhzgbxpp4erqzf5klfu
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