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Book ; Online: A Review of Stochastic Block Models and Extensions for Graph Clustering

Lee, Clement / Wilkinson, Darren J

2019

Abstract: There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, ...

Abstract	There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, the clustering approach, the inference approach, and whether the number of groups is selected or estimated. We also review models that combine block modelling with topic modelling and/or longitudinal modelling, regarding how these models deal with multiple types of data. How different approaches cope with various issues will be summarised and compared, to facilitate the demand of practitioners for a concise overview of the current status of these areas of literature. Comment: 93 pages, 3 figures, 4 tables
Keywords	Statistics - Machine Learning ; Computer Science - Machine Learning
Publishing date	2019-02-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A hierarchical model of non-homogeneous Poisson processes for Twitter retweets

Lee, Clement / Wilkinson, Darren J

2018

Abstract: We present a hierarchical model of non-homogeneous Poisson processes (NHPP) for information diffusion on online social media, in particular Twitter retweets. The retweets of each original tweet are modelled by a NHPP, for which the intensity function is ... ...

Abstract	We present a hierarchical model of non-homogeneous Poisson processes (NHPP) for information diffusion on online social media, in particular Twitter retweets. The retweets of each original tweet are modelled by a NHPP, for which the intensity function is a product of time-decaying components and another component that depends on the follower count of the original tweet author. The latter allows us to explain or predict the ultimate retweet count by a network centrality-related covariate. The inference algorithm enables the Bayes factor to be computed, in order to facilitate model selection. Finally, the model is applied to the retweet data sets of two hashtags. Comment: 48 pages, 13 figures, 2 tables
Keywords	Statistics - Applications ; Computer Science - Social and Information Networks ; Statistics - Computation
Publishing date	2018-02-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Bayesian inference for Markov jump processes with informative observations.

Golightly, Andrew / Wilkinson, Darren J

Statistical applications in genetics and molecular biology

2015 Volume 14, Issue 2, Page(s) 169–188

Abstract: In this paper we consider the problem of parameter inference for Markov jump process (MJP) representations of stochastic kinetic models. Since transition probabilities are intractable for most processes of interest yet forward simulation is ... ...

Abstract	In this paper we consider the problem of parameter inference for Markov jump process (MJP) representations of stochastic kinetic models. Since transition probabilities are intractable for most processes of interest yet forward simulation is straightforward, Bayesian inference typically proceeds through computationally intensive methods such as (particle) MCMC. Such methods ostensibly require the ability to simulate trajectories from the conditioned jump process. When observations are highly informative, use of the forward simulator is likely to be inefficient and may even preclude an exact (simulation based) analysis. We therefore propose three methods for improving the efficiency of simulating conditioned jump processes. A conditioned hazard is derived based on an approximation to the jump process, and used to generate end-point conditioned trajectories for use inside an importance sampling algorithm. We also adapt a recently proposed sequential Monte Carlo scheme to our problem. Essentially, trajectories are reweighted at a set of intermediate time points, with more weight assigned to trajectories that are consistent with the next observation. We consider two implementations of this approach, based on two continuous approximations of the MJP. We compare these constructs for a simple tractable jump process before using them to perform inference for a Lotka-Volterra system. The best performing construct is used to infer the parameters governing a simple model of motility regulation in Bacillus subtilis.
MeSH term(s)	Algorithms ; Bayes Theorem ; Computer Simulation ; Kinetics ; Markov Chains ; Models, Biological ; Monte Carlo Method ; Probability
Language	English
Publishing date	2015-04
Publishing country	Germany
Document type	Journal Article
ISSN	1544-6115
ISSN (online)	1544-6115
DOI	10.1515/sagmb-2014-0070
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Article ; Online: Stochastic modelling for quantitative description of heterogeneous biological systems.

Wilkinson, Darren J

Nature reviews. Genetics

2009 Volume 10, Issue 2, Page(s) 122–133

Abstract: Two related developments are currently changing traditional approaches to computational systems biology modelling. First, stochastic models are being used increasingly in preference to deterministic models to describe biochemical network dynamics at the ... ...

Abstract	Two related developments are currently changing traditional approaches to computational systems biology modelling. First, stochastic models are being used increasingly in preference to deterministic models to describe biochemical network dynamics at the single-cell level. Second, sophisticated statistical methods and algorithms are being used to fit both deterministic and stochastic models to time course and other experimental data. Both frameworks are needed to adequately describe observed noise, variability and heterogeneity of biological systems over a range of scales of biological organization.
MeSH term(s)	Computational Biology/methods ; Data Interpretation, Statistical ; Gene Expression Regulation ; Models, Biological ; Proto-Oncogene Proteins c-mdm2/metabolism ; Stochastic Processes ; Systems Biology ; Tumor Suppressor Protein p53/metabolism
Chemical Substances	Tumor Suppressor Protein p53 ; MDM2 protein, human (EC 2.3.2.27) ; Proto-Oncogene Proteins c-mdm2 (EC 2.3.2.27)
Language	English
Publishing date	2009-01-13
Publishing country	England
Document type	Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2035157-4
ISSN	1471-0064 ; 1471-0056
ISSN (online)	1471-0064
ISSN	1471-0056
DOI	10.1038/nrg2509
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Zs.A 5474: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article: A network epidemic model for online community commissioning data.

Lee, Clement / Garbett, Andrew / Wilkinson, Darren J

Statistics and computing

2017 Volume 28, Issue 4, Page(s) 891–904

Abstract: A statistical model assuming a preferential attachment network, which is generated by adding nodes sequentially according to a few simple rules, usually describes real-life networks better than a model assuming, for example, a Bernoulli random graph, in ... ...

Abstract	A statistical model assuming a preferential attachment network, which is generated by adding nodes sequentially according to a few simple rules, usually describes real-life networks better than a model assuming, for example, a Bernoulli random graph, in which any two nodes have the same probability of being connected, does. Therefore, to study the propagation of "infection" across a social network, we propose a network epidemic model by combining a stochastic epidemic model and a preferential attachment model. A simulation study based on the subsequent Markov Chain Monte Carlo algorithm reveals an identifiability issue with the model parameters. Finally, the network epidemic model is applied to a set of online commissioning data.
Language	English
Publishing date	2017-08-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	2017741-0
ISSN	1573-1375 ; 0960-3174
ISSN (online)	1573-1375
ISSN	0960-3174
DOI	10.1007/s11222-017-9770-6
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Article: Bayesian methods in bioinformatics and computational systems biology.

Wilkinson, Darren J

Briefings in bioinformatics

2007 Volume 8, Issue 2, Page(s) 109–116

Abstract: Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation ... ...

Abstract	Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology.
MeSH term(s)	Artificial Intelligence ; Bayes Theorem ; Computational Biology/methods ; Gene Expression Profiling/methods ; Oligonucleotide Array Sequence Analysis/methods ; Pattern Recognition, Automated/methods ; Proteome/metabolism ; Sequence Analysis/methods ; Signal Transduction/physiology ; Systems Biology/methods
Chemical Substances	Proteome
Language	English
Publishing date	2007-03
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2068142-2
ISSN	1467-5463
ISSN	1467-5463
DOI	10.1093/bib/bbm007
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Zs.A 6262: Show issues

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Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Likelihood free inference for Markov processes: a comparison.

Owen, Jamie / Wilkinson, Darren J / Gillespie, Colin S

Statistical applications in genetics and molecular biology

2015 Volume 14, Issue 2, Page(s) 189–209

Abstract: Approaches to Bayesian inference for problems with intractable likelihoods have become increasingly important in recent years. Approximate Bayesian computation (ABC) and "likelihood free" Markov chain Monte Carlo techniques are popular methods for ... ...

Abstract	Approaches to Bayesian inference for problems with intractable likelihoods have become increasingly important in recent years. Approximate Bayesian computation (ABC) and "likelihood free" Markov chain Monte Carlo techniques are popular methods for tackling inference in these scenarios but such techniques are computationally expensive. In this paper we compare the two approaches to inference, with a particular focus on parameter inference for stochastic kinetic models, widely used in systems biology. Discrete time transition kernels for models of this type are intractable for all but the most trivial systems yet forward simulation is usually straightforward. We discuss the relative merits and drawbacks of each approach whilst considering the computational cost implications and efficiency of these techniques. In order to explore the properties of each approach we examine a range of observation regimes using two example models. We use a Lotka-Volterra predator-prey model to explore the impact of full or partial species observations using various time course observations under the assumption of known and unknown measurement error. Further investigation into the impact of observation error is then made using a Schlögl system, a test case which exhibits bi-modal state stability in some regions of parameter space.
MeSH term(s)	Algorithms ; Bayes Theorem ; Computer Simulation ; Kinetics ; Likelihood Functions ; Markov Chains ; Models, Biological ; Monte Carlo Method ; Systems Biology
Language	English
Publishing date	2015-02-26
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1544-6115
ISSN (online)	1544-6115
DOI	10.1515/sagmb-2014-0072
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo.

Golightly, Andrew / Wilkinson, Darren J

Interface focus

2011 Volume 1, Issue 6, Page(s) 807–820

Abstract: Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course ...

Abstract	Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka-Volterra system and a prokaryotic auto-regulatory network.
Language	English
Publishing date	2011-09-29
Publishing country	England
Document type	Journal Article
ISSN	2042-8901
ISSN (online)	2042-8901
DOI	10.1098/rsfs.2011.0047
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Article ; Online: Bayesian identification of protein differential expression in multi-group isobaric labelled mass spectrometry data.

Jow, Howsun / Boys, Richard J / Wilkinson, Darren J

Statistical applications in genetics and molecular biology

2014 Volume 13, Issue 5, Page(s) 531–551

Abstract: In this paper we develop a Bayesian statistical inference approach to the unified analysis of isobaric labelled MS/MS proteomic data across multiple experiments. An explicit probabilistic model of the log-intensity of the isobaric labels' reporter ions ... ...

Abstract	In this paper we develop a Bayesian statistical inference approach to the unified analysis of isobaric labelled MS/MS proteomic data across multiple experiments. An explicit probabilistic model of the log-intensity of the isobaric labels' reporter ions across multiple pre-defined groups and experiments is developed. This is then used to develop a full Bayesian statistical methodology for the identification of differentially expressed proteins, with respect to a control group, across multiple groups and experiments. This methodology is implemented and then evaluated on simulated data and on two model experimental datasets (for which the differentially expressed proteins are known) that use a TMT labelling protocol.
MeSH term(s)	Bayes Theorem ; Models, Theoretical ; Proteins/chemistry ; Proteomics ; Tandem Mass Spectrometry/methods
Chemical Substances	Proteins
Language	English
Publishing date	2014-09-05
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1544-6115
ISSN (online)	1544-6115
DOI	10.1515/sagmb-2012-0066
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Article ; Online: Systems Biology Markup Language (SBML) Level 3 Package

Smith Lucian P. / Moodie Stuart L. / Bergmann Frank T. / Gillespie Colin / Keating Sarah M. / König Matthias / Myers Chris J. / Swat Maciek J. / Wilkinson Darren J. / Hucka Michael

Journal of Integrative Bioinformatics, Vol 17, Iss 2-

Distributions, Version 1, Release 1

2020 Volume 3

Abstract: Biological models often contain elements that have inexact numerical values, since they are based on values that are stochastic in nature or data that contains uncertainty. The Systems Biology Markup Language (SBML) Level 3 Core specification does not ... ...

Abstract	Biological models often contain elements that have inexact numerical values, since they are based on values that are stochastic in nature or data that contains uncertainty. The Systems Biology Markup Language (SBML) Level 3 Core specification does not include an explicit mechanism to include inexact or stochastic values in a model, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactic constructs. The SBML Distributions package for SBML Level 3 adds the necessary features to allow models to encode information about the distribution and uncertainty of values underlying a quantity.
Keywords	distributions ; modeling ; sbml ; systems biology ; uncertainty ; Biotechnology ; TP248.13-248.65
Language	English
Publishing date	2020-07-01T00:00:00Z
Publisher	De Gruyter
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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