Download or read book Learning gene interactions from gene expression data using dynamic Bayesian networks written by Gudrún Bergmann Sigursteinsdóttir and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Modeling and Learning Realistic Genetic Interactions Using Dynamic Bayesian Network and Information Theory written by Nizamul Morshed and published by . This book was released on 2013 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deciphering genetic interactions is of fundamental importance in computational systems biology, with wide applications in a number of other associated areas. Realistic modeling of these interactions poses novel challenges while dealing with the problem. Further, learning these interactions using computational methods becomes increasingly complex with the adoption of advanced and more realistic modeling techniques. In this thesis, we propose methods to address this challenge using a graphical model having sound probabilistic underpinnings, commonly known as dynamic Bayesian networks. Inference of genetic interactions is usually carried out using DNA microarray data. This data provides snapshots of mRNA expression levels of a large number of genes from a single experiment. However, the number of samples from such experiments is small, and additionally, they contain missing values and noise. Bayesian networks are considered as one of the most promising ways by which these issues can be tackled. However, traditional Bayesian networks have their own limitations; for example, they neither take time information into account nor can they capture feedback. Further, accurate determination of the direction of regulation requires a significant number of tests to be performed. Dynamic Bayesian networks (DBN) are extensions of Bayesian networks that can effectively address these limitations. In this thesis, we develop novel techniques for gene regulatory network reconstruction using DBN based modeling approach. We start with a basic DBN based model, and improve it so that it can represent and model both instantaneous and time-delayed genetic interactions. Initially, we aim to detect the occurrence of instantaneous and single-step time-delayed interactions, and subsequently this approach is further extended to model the instantaneous and multi-step time-delayed interactions. This approach of modeling both instantaneous and multi-step time-delayed genetic interactions is superior to traditional DBN based GRN reconstruction techniques, where only the time delayed interactions are learnt.%, thereby advancing the state of the art for modeling genetic regulations using DBNs.In addition to modeling interactions, one needs a learning mechanism for inferring genetic interactions. To facilitate detection of nonlinear gene to gene interactions (in addition to linear interactions), which are prevalent in all genetic networks, we propose using well known properties, including fundamental results related to information theoretic measures for testing conditional independence relations in a DBN. This enables us to formulate efficient learning techniques for reconstructing GRNs. Using these theoretical underpinnings, we first implement simple hill-climbing techniques that enable detection of various types of interactions among genes. Subsequently, we use these results to devise novel score and search based evolutionary computation techniques, which can effectively explore a significantly larger search space. We carry out investigations using both synthetic networks as well as real-life networks. For real-life network study, we use four different microarray data sources, covering three organisms, namely, yeast, E. coli and cyanobacteria. We use networks of varying sizes, ranging from five-gene small networks (yeast) to large scale networks of cyanobacteria (730 genes). The evaluation of the performance is carried out using four widely used performance measures. For some networks where we do not have sufficient information for calculating these performance measures, we use literature mining for performing comparative evaluations of the proposed approaches. For the large scale network of cyanobacteria, we use gene ontology (GO) based analysis of gene functionalities, in addition to degree distribution analysis of the inferred network.Due to the inherent difficulties associated with inferring GRNs using DNA microarray data, it is often supplemented by other sources of data; for example, genomic data and protein-protein interaction data. In this thesis, we propose a framework that jointly learns the structure of a GRN and a protein-protein interaction network (PPIN). Using this process, the GRN reconstruction technique can effectively make use of the vast wealth of knowledge available from these external sources of data. This knowledge is fed to the GRN reconstruction process probabilistically, thereby enabling it to weigh each different data source according to the reliability of that source. The approach is applied on yeast networks where four different interaction data sources and a number of genomic data sources are used. Together with the novel modeling and learning techniques proposed in this thesis, the probabilistic integration of different types of knowledge sources and the co-learning of GRN with PPIN represents a significant step towards the reconstruction of GRNs using DBNs.

Download or read book Using a Dynamic Bayesian Network to Learn Gene Interactions written by Linus Göransson and published by . This book was released on 2002 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Reverse Engineering of Temporal Gene Expression Data Using Dynamic Bayesian Networks and Evolutionary Search written by Maryam Salehi and published by . This book was released on 2008 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Capturing the mechanism of gene regulation in a living cell is essential to predict the behavior of cell in response to intercellular or extra cellular factors. Such prediction capability can potentially lead to development of improved diagnostic tests and therapeutics [21]. Amongst reverse engineering approaches that aim to model gene regulation are Dynamic Bayesian Networks (DBNs). DBNs are of particular interest as these models are capable of discovering the causal relationships between genes while dealing with noisy gene expression data. At the same time, the problem of discovering the optimum DBN model, makes structure learning of DBN a challenging topic. This is mainly due to the high dimensionality of the search space of gene expression data that makes exhaustive search strategies for identifying the best DBN structure, not practical. In this work, for the first time the application of a covariance-based evolutionary search algorithm is proposed for structure learning of DBNs. In addition, the convergence time of the proposed algorithm is improved compared to the previously reported covariance-based evolutionary search approaches. This is achieved by keeping a fixed number of good sample solutions from previous iterations. Finally, the proposed approach, M-CMA-ES, unlike gradient-based methods has a high probability to converge to a global optimum. To assess how efficient this approach works, a temporal synthetic dataset is developed. The proposed approach is then applied to this dataset as well as Brainsim dataset, a well known simulated temporal gene expression data [58]. The results indicate that the proposed method is quite efficient in reconstructing the networks in both the synthetic and Brainsim datasets. Furthermore, it outperforms other algorithms in terms of both the predicted structure accuracy and the mean square error of the reconstructed time series of gene expression data. For validation purposes, the proposed approach is also applied to a biological dataset composed of 14 cell-cycle regulated genes in yeast Saccharomyces Cerevisiae. Considering the KEGG1 pathway as the target network, the efficiency of the proposed reverse engineering approach significantly improves on the results of two previous studies of yeast cell cycle data in terms of capturing the correct interactions.

Download or read book Inference of Gene Regulatory Network Based on Gene Expression Dynamics in Response to Environmental Signals written by Yaqun Wang and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Thousands of genes are encoded on the genome and their products play important roles to cell survival, phenotypic characteristics of organisms and adaptive behaviors of organisms when environment changes. Detecting of particular sets of genes whose expressions are adaptive in response to environmental signals and identification of dynamic gene regulatory networks (GRN) can help us to understand the mechanistic base of gene-environment interactions and gene-gene interactions in a systematic way. However, it is a challenging work to analyze gene expression across two-dimensional spaces, time and environmental state. In this dissertation, we develop a functional clustering framework based on a mixture model to analyze time-course gene expression. The mathematical aspects of gene expression dynamics have been captured by Legendre polynomial and the impact of environment on gene expression has been considered jointly. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The method is illustrated with simulation studies and application on a real data set from a rabbit hemodynamic study.In addition, we propose two models for inference of GRN based on gene expression. We reform the Dynamic Bayesian Network (DBN) model for identification of GRN to overcome its limitation that evenly spaced measurements is required. The reformed model can accommodate to any possible irregularity and sparsity of time-course expression data by adaptively fitting gene expression curves, followed by a step of interpolating data at missing time points before conducting of DBN analysis. We also develop an ordinary differential equation (ODE) model to reconstruct GRNs based on functional clustering of genes. A set of ordinary differential equations are constructed to quantify the dynamic of GRN and the regulatory effects including positive and negative regulation are identified in a regression setting by using Smoothly Clipped Absolute Deviation (SCAD)-based variable selection. Both GRN models are equipped with unique power to integrate gene expression data from multiple environments and, therefore, provides an unprecedented tool to elucidate a comprehensive picture of GRN. By analyzing real data sets from a surgical study and through extensive simulation studies, the new models have been well demonstrated for their usefulness and utility.

Download or read book Probabilistic Methods for Financial and Marketing Informatics written by Richard E. Neapolitan and published by Elsevier. This book was released on 2010-07-26 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic reasoning to business problems. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors concentrate on showing examples and using the software package Netica to represent and solve problems. The book contains unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. This book is recommended for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to business or industry information. This includes computer science and other professionals in the data management and data mining field whose interests are business and marketing information in general, and who want to apply AI and probabilistic methods to their problems in order to better predict how well a product or service will do in a particular market, for instance. Typical fields where this technology is used are in advertising, venture capital decision making, operational risk measurement in any industry, credit scoring, and investment science. Unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance Shares insights about when and why probabilistic methods can and cannot be used effectively Complete review of Bayesian networks and probabilistic methods for those IT professionals new to informatics.

Download or read book Inferring Gene Network from Gene Expression Data Using Dynamic Bayesian Network with Different Scoring Metric Approaches written by Masarrah Abdul Mutalib and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Gene Network Inference written by Alberto Fuente and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians.

Download or read book Handbook of Research on Computational Methodologies in Gene Regulatory Networks written by Das, Sanjoy and published by IGI Global. This book was released on 2009-10-31 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization"--Provided by publisher.

Download or read book Computational Techniques for Inferring Regulatory Networks written by Irene M. Ong and published by . This book was released on 2007 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning and Network Driven Integrative Genomics written by Mehdi Pirooznia and published by Frontiers Media SA. This book was released on 2021-04-29 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 1828 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.

Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.

Download or read book Machine Learning Approach to Reconstructing Signalling Pathways and Interaction Networks in Biology written by Frank Dondelinger and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this doctoral thesis, I present my research into applying machine learning techniques for reconstructing species interaction networks in ecology, reconstructing molecular signalling pathways and gene regulatory networks in systems biology, and inferring parameters in ordinary differential equation (ODE) models of signalling pathways. Together, the methods I have developed for these applications demonstrate the usefulness of machine learning for reconstructing networks and inferring network parameters from data. The thesis consists of three parts. The first part is a detailed comparison of applying static Bayesian networks, relevance vector machines, and linear regression with L1 regularisation (LASSO) to the problem of reconstructing species interaction networks from species absence/presence data in ecology (Faisal et al., 2010). I describe how I generated data from a stochastic population model to test the different methods and how the simulation study led us to introduce spatial autocorrelation as an important covariate. I also show how we used the results of the simulation study to apply the methods to presence/absence data of bird species from the European Bird Atlas. The second part of the thesis describes a time-varying, non-homogeneous dynamic Bayesian network model for reconstructing signalling pathways and gene regulatory networks, based on L`ebre et al. (2010). I show how my work has extended this model to incorporate different types of hierarchical Bayesian information sharing priors and different coupling strategies among nodes in the network. The introduction of these priors reduces the inference uncertainty by putting a penalty on the number of structure changes among network segments separated by inferred changepoints (Dondelinger et al., 2010; Husmeier et al., 2010; Dondelinger et al., 2012b). Using both synthetic and real data, I demonstrate that using information sharing priors leads to a better reconstruction accuracy of the underlying gene regulatory networks, and I compare the different priors and coupling strategies. I show the results of applying the model to gene expression datasets from Drosophila melanogaster and Arabidopsis thaliana, as well as to a synthetic biology gene expression dataset from Saccharomyces cerevisiae. In each case, the underlying network is time-varying; for Drosophila melanogaster, as a consequence of measuring gene expression during different developmental stages; for Arabidopsis thaliana, as a consequence of measuring gene expression for circadian clock genes under different conditions; and for the synthetic biology dataset, as a consequence of changing the growth environment. I show that in addition to inferring sensible network structures, the model also successfully predicts the locations of changepoints. The third and final part of this thesis is concerned with parameter inference in ODE models of biological systems. This problem is of interest to systems biology researchers, as kinetic reaction parameters can often not be measured, or can only be estimated imprecisely from experimental data. Due to the cost of numerically solving the ODE system after each parameter adaptation, this is a computationally challenging problem. Gradient matching techniques circumvent this problem by directly fitting the derivatives of the ODE to the slope of an interpolant. I present an inference procedure for a model using nonparametric Bayesian statistics with Gaussian processes, based on Calderhead et al. (2008). I show that the new inference procedure improves on the original formulation in Calderhead et al. (2008) and I present the result of applying it to ODE models of predator-prey interactions, a circadian clock gene, a signal transduction pathway, and the JAK/STAT pathway.

Download or read book Bayesian Networks in R written by Radhakrishnan Nagarajan and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.

Download or read book Mathematical Foundations of Computer Science 2006 written by Rastislav Královic and published by Springer Science & Business Media. This book was released on 2006-08-11 with total page 827 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 31st International Symposium on Mathematical Foundations of Computer Science, MFCS 2006. The book presents 62 revised full papers together with the full papers or abstracts of 7 invited talks. All current aspects in theoretical computer science and its mathematical foundations are addressed, from algorithms and data structures, to complexity, automata, semantics, logic, formal specifications, models of computation, concurrency theory, computational geometry and more.

Download or read book Bayesian Inference for Gene Expression and Proteomics written by Kim-Anh Do and published by Cambridge University Press. This book was released on 2006-07-24 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.