Download or read book A Granger Causality Approach to Gene Regulatory Network Reconstruction Based on Data from Multiple Experiments written by Hak-fui Tam and published by . This book was released on 2012 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Granger Causality Approach to Gene Regulatory Network Reconstruction Based on Data from Multiple Experiments written by Hak-Fui Tam and published by Open Dissertation Press. This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "A Granger Causality Approach to Gene Regulatory Network Reconstruction Based on Data From Multiple Experiments" by Hak-fui, Tam, 譚克奎, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The discovery of gene regulatory network (GRN) using gene expression data is one of the promising directions for deciphering biological mechanisms, which underlie many basic aspects of scientific and medical advances. In this thesis, we focus on the reconstruction of GRN from time-series data using a Granger causality (GC) approach. As there is little existing research on combining data from multiple time-series experiments, we identify the need for developing a methodology with underlying theory to combine multiple experiments for statistical significant discovery. We derive a statistical theory for intersection of two discovered networks. Such a statistical framework is novel and intended for our GRN discovery problem. However, this theory is not limited to GRN or GC, and may be applied to other problems as long as one can take the intersection of discoveries obtained from multiple experiments (or datasets). We propose a number of novel methods for combining data from multiple experiments. Our single underlying model (SUM) method regresses data of multiple experiments in one go, enabling GC to fully utilize the information in the original data. Based on our statistical theory and SUM, we develop new meta-analysis methods, including union of pairwise common edges (UPCE) and leave-one-out hybrid of SUM and UPCE (LOOHSU). Applications on synthetic data and real data show that our new methods give discoveries of substantially higher precision than traditional meta-analysis. We also propose methods for estimating the precision of GC-discovered networks and thus fill in an important gap not considered in the literature. This allows us to assess how good a discovered network is in the case of unknown ground truth, which is typical in most biological applications. Our precision estimation by half-half splitting with combinations (HHSC) gives an estimate much closer to the true value compared with that computed from the Benjamini-Hochberg false discovery rate controlling procedure. Furthermore, using a network covering notion, we design a method that can identify a small number of links with high precision of around 0.8-0.9, which may relieve the burden of testing many hypothetical interactions of low precision in biological experiments. For the situation where the number of genes is much larger than the data length, in which case full-model GC cannot be applied, GC is often applied to the genes pairwisely. We analyze how spurious causalities (false discoveries) may arise. Consequently, we demonstrate that model validation can effectively remove spurious discoveries. With our proposed implementation that model orders are fixed by the Akaike information criterion and every model is subject to validation, we report a new observation that network hubs tend to act as sources rather than receivers of interactions. Subjects: Gene regulatory networks - Statistical methods
Download or read book A Granger Causality Approach to Gene Regulatory Network Reconstruction Based on Data from Multiple Experiments written by Hak-fui Tam and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Prior Knowledge Driven Granger Causality Analysis on Gene Regulatory Network Discovery written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n”T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, the propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a "1+1>2" effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast's responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.
Download or read book Applications of Granger Causality to Biological Data written by and published by . This book was released on 2010 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Genomic Signal Processing written by Ilya Shmulevich and published by Princeton University Press. This book was released on 2014-09-08 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathematical definitions and propositions for the main elements of GSP and by paying attention to the validity of models relative to the data. Ilya Shmulevich and Edward Dougherty cover real-world situations and explain their mathematical modeling in relation to systems biology and systems medicine. Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention.
Download or read book Towards Causality in Gene Regulatory Network Inference written by Alexander Po-Yen Wu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the coordination of biomolecules that underlies gene regulation is key to gaining mechanistic insights into cellular functions, phenotypes, and diseases. Advances in single-cell technologies promise to unveil mechanisms of gene regulation at unprecedented resolution by enabling measurements of genomic and/or epigenetic features for individual cells. However, unlocking insights from single-cell data requires algorithmic innovations. This thesis introduces a series of methods for uncovering gene regulatory relationships underlying cellular identity and function from single-cell data. Firstly, we present a framework for enhancing the detection of statistical associations in small sample size settings for gene regulatory network inference. We then describe the use of single-cell genetic perturbation screens for determining the causal roles of critical regulatory complexes, focusing specifically on its applications for revealing mechanistic insights about the mammalian SWI/SNF family of chromatin remodeling complexes. To bridge the gap between methods that identify statistical associations from observational data and those that infer causal relationships using interventions, we also introduce a new category of techniques that extends the econometric concept of Granger causality to complex graph-based dynamical systems, such as those found in single-cell trajectories. In particular, we describe a graph neural network-based generalization of Granger causality for single-cell multimodal data that enables the detection of noncoding genomic loci implicated in the regulation of specific genes. We then demonstrate how we use this approach to link genetic variants to gene dysregulation in disease, focusing on its applications to schizophrenia etiology. Lastly, we present an extension of this graph-based Granger causal framework that leverages RNA velocity dynamics for causal gene regulatory network inference and enables inquiries into the role of temporal control in gene regulatory function and disease.
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 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 Statistics and Causality written by Wolfgang Wiedermann and published by John Wiley & Sons. This book was released on 2016-05-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: b”STATISTICS AND CAUSALITYA one-of-a-kind guide to identifying and dealing with modern statistical developments in causality Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses. The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes: New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories End-of-chapter bibliographies that provide references for further discussions and additional research topics Discussions on the use and applicability of software when appropriate Statistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic.
Download or read book Molecular Epidemiology written by Paul A. Schulte and published by Academic Press. This book was released on 2012-12-02 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will serve as a primer for both laboratory and field scientists who are shaping the emerging field of molecular epidemiology. Molecular epidemiology utilizes the same paradigm as traditional epidemiology but uses biological markers to identify exposure, disease or susceptibility. Schulte and Perera present the epidemiologic methods pertinent to biological markers. The book is also designed to enumerate the considerations necessary for valid field research and provide a resource on the salient and subtle features of biological indicators.
Download or read book Systems Biology written by Bernhard Palsson and published by Cambridge University Press. This book was released on 2015-01-26 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive single-authored textbook on genome-scale models and the bottom-up approach to systems biology.
Download or read book Systems Metabolic Engineering written by Christoph Wittmann and published by Springer Science & Business Media. This book was released on 2012-06-15 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Systems Metabolic Engineering is changing the way microbial cell factories are designed and optimized for industrial production. Integrating systems biology and biotechnology with new concepts from synthetic biology enables the global analysis and engineering of microorganisms and bioprocesses at super efficiency and versatility otherwise not accessible. Without doubt, systems metabolic engineering is a major driver towards bio-based production of chemicals, materials and fuels from renewables and thus one of the core technologies of global green growth. In this book, Christoph Wittmann and Sang-Yup Lee have assembled the world leaders on systems metabolic engineering and cover the full story – from genomes and networks via discovery and design to industrial implementation practises. This book is a comprehensive resource for students and researchers from academia and industry interested in systems metabolic engineering. It provides us with the fundaments to targeted engineering of microbial cells for sustainable bio-production and stimulates those who are interested to enter this exiting research field.
Download or read book The Long Shadow of Informality written by Franziska Ohnsorge and published by World Bank Publications. This book was released on 2022-02-09 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: A large percentage of workers and firms operate in the informal economy, outside the line of sight of governments in emerging market and developing economies. This may hold back the recovery in these economies from the deep recessions caused by the COVID-19 pandemic--unless governments adopt a broad set of policies to address the challenges of widespread informality. This study is the first comprehensive analysis of the extent of informality and its implications for a durable economic recovery and for long-term development. It finds that pervasive informality is associated with significantly weaker economic outcomes--including lower government resources to combat recessions, lower per capita incomes, greater poverty, less financial development, and weaker investment and productivity.
Download or read book Introduction to Graphical Modelling written by David Edwards and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: A useful introduction to this topic for both students and researchers, with an emphasis on applications and practicalities rather than on a formal development. It is based on the popular software package for graphical modelling, MIM, freely available for downloading from the Internet. Following a description of some of the basic ideas of graphical modelling, subsequent chapters describe particular families of models, including log-linear models, Gaussian models, and models for mixed discrete and continuous variables. Further chapters cover hypothesis testing and model selection. Chapters 7 and 8 are new to this second edition and describe the use of directed, chain, and other graphs, complete with a summary of recent work on causal inference.
Download or read book Angiogenesis Assays written by Carolyn A. Staton and published by John Wiley & Sons. This book was released on 2007-01-11 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Angiogenesis, the development of new blood vessels from the existing vasculature, is essential for physiological growth and over 18,000 research articles have been published describing the role of angiogenesis in over 70 different diseases, including cancer, diabetic retinopathy, rheumatoid arthritis and psoriasis. One of the most important technical challenges in such studies has been finding suitable methods for assessing the effects of regulators of eh angiogenic response. While increasing numbers of angiogenesis assays are being described both in vitro and in vivo, it is often still necessary to use a combination of assays to identify the cellular and molecular events in angiogenesis and the full range of effects of a given test protein. Although the endothelial cell - its migration, proliferation, differentiation and structural rearrangement - is central to the angiogenic process, it is not the only cell type involved. the supporting cells, the extracellular matrix and the circulating blood with its cellular and humoral components also contribute. In this book, experts in the use of a diverse range of assays outline key components of these and give a critical appraisal of their strengths and weaknesses. Examples include assays for the proliferation, migration and differentiation of endothelial cells in vitro, vessel outgrowth from organ cultures, assessment of endothelial and mural cell interactions, and such in vivo assays as the chick chorioallantoic membrane, zebrafish, corneal, chamber and tumour angiogenesis models. These are followed by a critical analysis of the biological end-points currently being used in clinical trials to assess the clinical efficacy of anti-angiogenic drugs, which leads into a discussion of the direction future studies should take. This valuable book is of interest to research scientists currently working on angiogenesis in both the academic community and in the biotechnology and pharmaceutical industries. Relevant disciplines include cell and molecular biology, oncology, cardiovascular research, biotechnology, pharmacology, pathology and physiology.
Download or read book Handbook of Statistical Genetics written by David J. Balding and published by John Wiley & Sons. This book was released on 2008-06-10 with total page 1616 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook for Statistical Genetics is widely regarded as the reference work in the field. However, the field has developed considerably over the past three years. In particular the modeling of genetic networks has advanced considerably via the evolution of microarray analysis. As a consequence the 3rd edition of the handbook contains a much expanded section on Network Modeling, including 5 new chapters covering metabolic networks, graphical modeling and inference and simulation of pedigrees and genealogies. Other chapters new to the 3rd edition include Human Population Genetics, Genome-wide Association Studies, Family-based Association Studies, Pharmacogenetics, Epigenetics, Ethic and Insurance. As with the second Edition, the Handbook includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between the chapters, tying the different areas together. With heavy use of up-to-date examples, real-life case studies and references to web-based resources, this continues to be must-have reference in a vital area of research. Edited by the leading international authorities in the field. David Balding - Department of Epidemiology & Public Health, Imperial College An advisor for our Probability & Statistics series, Professor Balding is also a previous Wiley author, having written Weight-of-Evidence for Forensic DNA Profiles, as well as having edited the two previous editions of HSG. With over 20 years teaching experience, he’s also had dozens of articles published in numerous international journals. Martin Bishop – Head of the Bioinformatics Division at the HGMP Resource Centre As well as the first two editions of HSG, Dr Bishop has edited a number of introductory books on the application of informatics to molecular biology and genetics. He is the Associate Editor of the journal Bioinformatics and Managing Editor of Briefings in Bioinformatics. Chris Cannings – Division of Genomic Medicine, University of Sheffield With over 40 years teaching in the area, Professor Cannings has published over 100 papers and is on the editorial board of many related journals. Co-editor of the two previous editions of HSG, he also authored a book on this topic.
