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Improving tree probability estimation with stochastic optimization and variance reduction

STATISTICS AND COMPUTING 2024年第6期34卷 1-18页

作者： Xie, Tianyu Yuan, Musu Deng, Minghua Zhang, Cheng Peking Univ Sch Math Sci Beijing Peoples R China Peking Univ Ctr Quantitat Biol Beijing Peoples R China Peking Univ Ctr Stat Sci Beijing Peoples R China

Probability estimation of tree topologies is one of the fundamental tasks in phylogenetic inference. The recently proposed subsplit Bayesian networks (SBNs) provide a powerful probabilistic graphical model for tree topology probability estimation by properly leveraging the hierarchical structure of phylogenetic trees. However, the expectation maximization method currently used for learning SBN parameters does not scale up to large data sets. In this paper, we introduce several computationally efficient methods for training SBNs and show that variance reduction could be the key for better performance. Furthermore, we also introduce the variance reduction technique to improve the optimization of SBN parameters for variational Bayesian phylogenetic inference. Extensive synthetic and real data experiments demonstrate that our methods outperform previous baseline methods on the tasks of tree topology probability estimation as well as Bayesian phylogenetic inference using SBNs.

关键词： Stochastic expectation maximization Variational Bayesian phylogenetic inference Variance reduction Tree probability estimation probabilistic graphical models

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Reverse-Engineering Transcriptional Modules from Gene Expression Data

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CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY 2009年第1期1158卷 36-43页

作者： Michoel, Tom De Smet, Riet Joshi, Anagha Marchal, Kathleen Van de Peer, Yves Univ Ghent VIB Dept Plant Syst Biol B-9052 Ghent Belgium UGent Dept Mol Genet Ghent Belgium KULeuven ESAT SCD Louvain Belgium KULeuven Dept Microbial & Mol Syst CMPG Louvain Belgium

"Module networks" are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions. We present a method to infer ensembles of such networks and an averaging procedure to extract the statistically most significant modules and their regulators. We show that the inferred probabilistic models extend beyond the dataset used to learn the models.

关键词： reverse engineering transcriptional modules probabilistic graphical models ensemble methods

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Using Directed Acyclic Graphs in Epidemiological Research in Psychosis: An Analysis of the Role of Bullying in Psychosis

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SCHIZOPHRENIA BULLETIN 2017年第6期43卷 1273-1279页

作者： Moffa, Giusi Catone, Gennaro Kuipers, Jack Kuipers, Elizabeth Freeman, Daniel Marwaha, Steven Lennox, Belinda R. Broome, Matthew R. Bebbington, Paul UCL Div Psychiat 67-73 Riding House St London W1T 7NF England Univ Hosp Basel Inst Clin Epidemiol & Biostat Basel Switzerland Univ Basel Basel Switzerland Univ Naples SUN Dept Mental & Phys Hlth & Prevent Med Naples Italy Suor Orsola Benicasa Univ Fac Educ Sci Naples Italy Swiss Fed Inst Technol D BSSE Basel Switzerland Kings Coll London Inst Psychiat Psychol & Neurosci Dept Psychol London England South London & Maudsley NHS Fdn Trust Biomed Res Ctr Beckenham Kent England Univ Oxford Warneford Hosp Dept Psychiat Oxford England Univ Warwick Warwick Med Sch Div Mental Hlth & Wellbeing Coventry W Midlands England Oxford Hlth NHS Fdn Trust Warneford Hosp Oxford England

Modern psychiatric epidemiology researches complex interactions between multiple variables in large datasets. This creates difficulties for causal inference. We argue for the use of probabilistic models represented by directed acyclic graphs (DAGs). These capture the dependence structure of multiple variables and, used appropriately, allow more robust conclusions about the direction of causation. We analyzed British national survey data to assess putative mediators of the association between bullying victimization and persecutory ideation. We compared results using DAGs and the Karlson-Holm-Breen (KHB) logistic regression commands in STATA. We analyzed data from the 2007 English National Survey of Psychiatric Morbidity, using the equivalent 2000 survey in an instant replication. Additional details of methods and results are provided in the supplementary material. DAG analysis revealed a richer structure of relationships than could be inferred using the KHB logistic regression commands. Thus, bullying had direct effects on worry, persecutory ideation, mood instability, and drug use. Depression, sleep and anxiety lay downstream, and therefore did not mediate the link between bullying and persecutory ideation. Mediation by worry and mood instability could not be definitively ascertained. Bullying led to hallucinations indirectly, via persecutory ideation and depression. DAG analysis of the 2000 dataset suggested the technique generates stable results. While causality cannot be fully determined from cross-sectional data, DAGs indicate the relationships providing the best fit. They thereby advance investigation of the complex interactions seen in psychiatry, including the mechanisms underpinning psychiatric symptoms. It may consequently be used to optimize the choice of intervention targets.

关键词： probabilistic graphical models directed acyclic graphs mediation bullying persecutory ideation psychosis worry depression anxiety

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Learning generative models for protein fold families

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2011年第4期79卷 1061-1078页

作者： Balakrishnan, Sivaraman Kamisetty, Hetunandan Carbonell, Jaime G. Lee, Su-In Langmead, Christopher James Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Lane Ctr Computat Biol Pittsburgh PA 15213 USA Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA Univ Washington Dept Genome Sci Seattle WA 98195 USA

We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized models of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position- specific conservation statistics and the correlated mutation statistics between sequential and long-range pairs of residues. Existing techniques for learning graphical models from MSA either make strong, and often inappropriate assumptions about the conditional independencies within the MSA (e. g., Hidden Markov models), or else use suboptimal algorithms to learn the parameters of the model. In contrast, GREMLIN makes no a priori assumptions about the conditional independencies within the MSA. We formulate and solve a convex optimization problem, thus guaranteeing that we find a globally optimal model at convergence. The resulting model is also generative, allowing for the design of new protein sequences that have the same statistical properties as those in the MSA. We perform a detailed analysis of covariation statistics on the extensively studied WW and PDZ domains and show that our method out-performs an existing algorithm for learning undirected probabilistic graphical models from MSA. We then apply our approach to 71 additional families from the PFAM database and demonstrate that the resulting models significantly out-perform Hidden Markov models in terms of predictive accuracy.

关键词： protein sequence probabilistic graphical models Markov random fields regularization generative model

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Inference of Natural Selection from Interspersed Genomic Elements Based on Polymorphism and Divergence

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MOLECULAR BIOLOGY AND EVOLUTION 2013年第5期30卷 1159-1171页

作者： Gronau, Ilan Arbiza, Leonardo Mohammed, Jaaved Siepel, Adam Cornell Univ Dept Biol Stat & Computat Biol Ithaca NY 14853 USA Triinst Training Program Computat Biol & Med New York NY USA

Complete genome sequences contain valuable information about natural selection, but this information is difficult to access for short, widely scattered noncoding elements such as transcription factor binding sites or small noncoding RNAs. Here, we introduce a new computational method, called Inference of Natural Selection from Interspersed Genomically coHerent elemenTs (INSIGHT), for measuring the influence of natural selection on such elements. INSIGHT uses a generative probabilistic model to contrast patterns of polymorphism and divergence in the elements of interest with those in flanking neutral sites, pooling weak information from many short elements in a manner that accounts for variation among loci in mutation rates and coalescent times. The method is able to disentangle the contributions of weak negative, strong negative, and positive selection based on their distinct effects on patterns of polymorphism and divergence. It obtains information about divergence from multiple outgroup genomes using a general statistical phylogenetic approach. The INSIGHT model is efficiently fitted to genome-wide data using an approximate expectation maximization algorithm. Using simulations, we show that the method can accurately estimate the parameters of interest even in complex demographic scenarios, and that it significantly improves on methods based on summary statistics describing polymorphism and divergence. To demonstrate the usefulness of INSIGHT, we apply it to several classes of human noncoding RNAs and to GATA2-binding sites in the human genome.

关键词： molecular evolution population genetics noncoding DNA regulatory sequences probabilistic graphical models

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A Bayesian Hierarchical Model for Criminal Investigations

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BAYESIAN ANALYSIS 2021年第1期16卷 1-30页

作者： Bunnin, F. O. Smith, J. Q. Alan Turing Inst British Lib 96 Euston Rd London NW1 2DB England Imperial Coll London Data Sci Inst South Kensington Campus London SW7 2AZ England Univ Warwick Dept Stat Coventry CV4 7AL W Midlands England

Potential violent criminals will often need to go through a sequence of preparatory steps before they can execute their plans. During this escalation process police have the opportunity to evaluate the threat posed by such people through what they know, observe and learn from intelligence reports about their activities. In this paper we customise a three-level Bayesian hierarchical model to describe this process. This is able to propagate both routine and unexpected evidence in real time. We discuss how to set up such a model so that it calibrates to domain expert judgments. The model illustrations include a hypothetical example based on a potential vehicle based terrorist attack.

关键词： Chain Event Graphs hierarchical models Markov processes Markov switching models probabilistic graphical models decision support systems

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Accounting for conformational entropy in predicting binding free energies of protein-protein interactions

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2011年第2期79卷 444-462页

作者： Kamisetty, Hetunandan Ramanathan, Arvind Bailey-Kellogg, Chris Langmead, Christopher James Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Lane Ctr Computat Biol Pittsburgh PA 15213 USA Dartmouth Coll Dept Comp Sci Hanover NH 03755 USA

Protein-protein interactions are governed by the change in free energy upon binding, Delta G = Delta H - T Delta S. These interactions are often marginally stable, so one must examine the balance between the change in enthalpy, Delta H, and the change in entropy, Delta S, when investigating known complexes, characterizing the effects of mutations, or designing optimized variants. To perform a large-scale study into the contribution of conformational entropy to binding free energy, we developed a technique called GOBLIN (graphical model for BiomoLecular INteractions) that performs physics-based free energy calculations for protein-protein complexes under both side-chain and backbone flexibility. GOBLIN uses a probabilistic graphical model that exploits conditional independencies in the Boltzmann distribution and employs variational inference techniques that approximate the free energy of binding in only a few minutes. We examined the role of conformational entropy on a benchmark set of more than 700 mutants in eight large, well-studied complexes. Our findings suggest that conformational entropy is important in protein-protein interactions-the root mean square error (RMSE) between calculated and experimentally measured Delta Delta Gs decreases by 12% when explicit entropic contributions were incorporated. GOBLIN models all atoms of the protein complex and detects changes to the binding entropy along the interface as well as positions distal to the binding interface. Our results also suggest that a variational approach to entropy calculations may be quantitatively more accurate than the knowledge-based approaches used by the well-known programs FOLDX and ROSETTA-GOBLIN'S RMSEs are 10 and 36% lower than these programs, respectively. Proteins 2011;79:444-462. (C) 2010 Wiley-Liss, Inc.

关键词： probabilistic graphical models variational inference thermodynamics mutation prediction protein complex

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Learning Bayesian networks with low inference complexity

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2016年第1期5卷 15-26页

作者： Benjumeda, Marco Larranaga, Pedro Bielza, Concha Univ Politecn Madrid Dept Inteligencia Artificial Computat Intelligence Grp Madrid Spain

One of the main research topics in machine learning nowadays is the improvement of the inference and learning processes in probabilistic graphical models. Traditionally, inference and learning have been treated separately, but given that the structure of the model conditions the inference complexity, most learning methods will sometimes produce inefficient inference models. In this paper we propose a framework for learning low inference complexity Bayesian networks. For that, we use a representation of the network factorization that allows efficiently evaluating an upper bound in the inference complexity of each model during the learning process. Experimental results show that the proposed methods obtain tractable models that improve the accuracy of the predictions provided by approximate inference in models obtained with a well-known Bayesian network learner.

关键词： probabilistic graphical models Bayesian networks Arithmetic circuits Network polynomials Structure learning Thin models

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The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2018年第522期113卷 855-867页

作者： Bouchard-Cote, Alexandre Vollmer, Sebastian J. Doucet, Arnaud Univ British Columbia Dept Stat Vancouver BC Canada Univ Warwick Math Inst Coventry CV4 7AL W Midlands England Univ Warwick Dept Stat Coventry CV4 7AL W Midlands England Alan Turing Inst London England Univ Oxford Dept Stat Oxford England

Many Markov chain Monte Carlo techniques currently available rely on discrete-time reversible Markov processes whose transition kernels are variations of the Metropolis-Hastings algorithm. We explore and generalize an alternative scheme recently introduced in the physics literature (Peters and de With 2012) where the target distribution is explored using a continuous-time nonreversible piecewise-deterministic Markov process. In the Metropolis-Hastings algorithm, a trial move to a region of lower target density, equivalently of higher "energy," than the current state can be rejected with positive probability. In this alternative approach, a particle moves along straight lines around the space and, when facing a high energy barrier, it is not rejected but its path is modified by bouncing against this barrier. By reformulating this algorithm using inhomogeneous Poisson processes, we exploit standard sampling techniques to simulate exactly this Markov process in a wide range of scenarios of interest. Additionally, when the target distribution is given by a product of factors dependent only on subsets of the state variables, such as the posterior distribution associated with a probabilistic graphical model, this method can be modified to take advantage of this structure by allowing computationally cheaper "local" bounces, which only involve the state variables associated with a factor, while the other state variables keep on evolving. In this context, by leveraging techniques from chemical kinetics, we propose several computationally efficient implementations. Experimentally, this new class of Markov chain Monte Carlo schemes compares favorably to state-of-the-art methods on various Bayesian inference tasks, including for high-dimensional models and large datasets. Supplementary materials for this article are available online.

关键词： Inhomogeneous Poisson process Markov chain Monte Carlo Piecewise deterministic Markov process probabilistic graphical models Rejection-free simulation

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Pairwise sequence alignment - it's all about us!

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BRIEFINGS IN BIOINFORMATICS 2006年第1期7卷 113-115页

作者： Mullan, Lisa EMBL European Bioinformat Inst Cambridge CB10 1SD England

Pairwise alignment is one of the most fundamental tools of bioinformatics and underpins a variety of other, more sophisticated methods of annotation. Pairwise alignment in its most rigorous form uses a method called ... 详细信息

关键词： machine learning bioinformatics supervised classification clustering probabilistic graphical models optimisation heuristic genomics proteomics microarray system biology evolution text mining

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