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 Benefits of Bayesian Network Models written by Philippe Weber and published by John Wiley & Sons. This book was released on 2016-08-29 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A large number of scientific publications show the interest in the applications of BN in this field. Unfortunately, this modeling formalism is not fully accepted in the industry. The questions facing today's engineers are focused on the validity of BN models and the resulting estimates. Indeed, a BN model is not based on a specific semantic in dependability but offers a general formalism for modeling problems under uncertainty. This book explains the principles of knowledge structuration to ensure a valid BN and DBN model and illustrate the flexibility and efficiency of these representations in dependability, risk analysis and control of multi-state systems and dynamic systems. Across five chapters, the authors present several modeling methods and industrial applications are referenced for illustration in real industrial contexts.
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 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 Bayesian Networks written by Olivier Pourret and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.
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 Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis written by Uffe B. Kjærulff and published by Springer Science & Business Media. This book was released on 2012-11-30 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.
Download or read book Bayesian Networks and Decision Graphs written by Thomas Dyhre Nielsen and published by Springer Science & Business Media. This book was released on 2007-06-06 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.
Download or read book Learning Bayesian Networks written by Richard E. Neapolitan and published by Prentice Hall. This book was released on 2004 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-01 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first comprehensive treatment of probabilistic Boolean networks (PBNs), an important model class for studying genetic regulatory networks. This book covers basic model properties, including the relationships between network structure and dynamics, steady-state analysis, and relationships to other model classes." "Researchers in mathematics, computer science, and engineering are exposed to important applications in systems biology and presented with ample opportunities for developing new approaches and methods. The book is also appropriate for advanced undergraduates, graduate students, and scientists working in the fields of computational biology, genomic signal processing, control and systems theory, and computer science.
Download or read book Introduction to Bayesian Networks written by Finn V. Jensen and published by Springer. This book was released on 1997-08-15 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disk contains: Tool for building Bayesian networks -- Library of examples -- Library of proposed solutions to some exercises.
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 Probabilistic Modeling in Bioinformatics and Medical Informatics written by Dirk Husmeier and published by Springer Science & Business Media. This book was released on 2005-02 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written for researchers and students in statistics, machine learning, and the biological sciences. This book provides a self-contained introduction to the methodology of Bayesian networks. It offers both elementary tutorials as well as more advanced applications and case studies.
Download or read book Probabilistic Graphical Models for Genetics Genomics and Postgenomics written by Raphaël Mourad and published by OUP Oxford. This book was released on 2014-09-18 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.
Download or read book Bioinformatics Using Computational Intelligence Paradigms written by Udo Seiffert and published by Springer Science & Business Media. This book was released on 2005-01-17 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bioinformatics and computational intelligence are undoubtedly remarkably fast growing fields of research and real-world applications with enormous potential for current and future developments. Bioinformatics Using Computational Intelligence Paradigms contains recent theoretical approaches and guiding applications of biologically inspired information processing systems (computational intelligence) against the background of bioinformatics. This carefully edited monograph combines the latest results of bioinformatics and computational intelligence, and offers promising cross-fertilization and interdisciplinary work between these growing fields.
Download or read book Modeling Multiple Time Units Delayed Gene Regulatory Network Using Dynamic Bayesian Network written by Zhengzheng Xing and published by . This book was released on 2006 with total page 184 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.
