Download or read book Analysis of Large Scale Genetic Perturbation with Linear Regression of Microarray and Bayesian Networks written by Ruifu Jiang and published by . This book was released on 2018 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper aims to examine how large-scale genetic perturbations reveal regulatory network and an abundance of gene-specific repressors by analyzing data from a published paper (Kemmeren et al., 2014) . The main goal is to uniformly determine the effect of different components on the expression of all other genes. The idea of their experiment is doing gene deletion of one-quarter of yeast genes individually and then observing the mRNA expression genomewide. Then genetic perturbation would be resulted, which also shows some properties including the architecture of protein complexes and pathways, identification of expression changes compatible with viability, and the varying responsiveness to genetic perturbation. And all data collected from this experiment is constructed as a genetic perturbation network which present a varying connectivities among regulators. Finally it provides a regulation network with analysis result from R package limma and sparsebn.
Download or read book Analysis of Microarray Data written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2008-09-08 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used. From the contents: * Understanding and Preprocessing Microarray Data * Clustering of Microarray Data * Reconstruction of the Yeast Cell Cycle by Partial Correlations of Higher Order * Bilayer Verification Algorithm * Probabilistic Boolean Networks as Models for Gene Regulation * Estimating Transcriptional Regulatory Networks by a Bayesian Network * Analysis of Therapeutic Compound Effects * Statistical Methods for Inference of Genetic Networks and Regulatory Modules * Identification of Genetic Networks by Structural Equations * Predicting Functional Modules Using Microarray and Protein Interaction Data * Integrating Results from Literature Mining and Microarray Experiments to Infer Gene Networks The book is for both, scientists using the technique as well as those developing new analysis techniques.
Download or read book Methods of Microarray Data Analysis written by Simon M. Lin and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis is one of the first books dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods ranging from data normalization, feature selection and discriminative analysis to machine learning techniques. Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis focuses on two well-known data sets, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.
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 Statistical Analysis of Gene Expression Microarray Data written by Terry Speed and published by CRC Press. This book was released on 2003-03-26 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although less than a decade old, the field of microarray data analysis is now thriving and growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as statisticians all need an accessible, systematic treatment of the techniques used for analyzing the vast amounts of data generated by large-scale gene expression studies
Download or read book Analysis of Molecular Expression Patterns and Integration with Other Knowledge Bases Using Probabilistic Bayesian Network Models written by and published by . This book was released on 2000 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can molecular expression experiments be interpreted with greater than ten to the fourth measurements per chip? How can one get the most quantitative information possible from the experimental data with good confidence? These are important questions whose solutions require an interdisciplinary combination of molecular and cellular biology, computer science, statistics, and complex systems analysis. The explosion of data from microarray techniques present the problem of interpreting the experiments. The availability of large-scale knowledge bases provide the opportunity to maximize the information extracted from these experiments. We have developed new methods of discovering biological function, metabolic pathways, and regulatory networks from these data and knowledge bases. These techniques are applicable to analyses for biomedical engineering, clinical, and fundamental cell and molecular biology studies. Our approach uses probabilistic, computational methods that give quantitative interpretations of data in a biological context. We have selected Bayesian statistical models with graphical network representations as a framework for our methods. As a first step, we use a nave Bayesian classifier to identify statistically significant patterns in gene expression data. We have developed methods which allow us to (a) characterize which genes or experiments distinguish each class from the others, (b) cross-index the resulting classes with other databases to assess biological meaning of the classes, and (c) display a gross overview of cellular dynamics. We have developed a number of visualization tools to convey the results. We report here our methods of classification and our first attempts at integrating the data and other knowledge bases together with new visualization tools. We demonstrate the utility of these methods and tools by analysis of a series of yeast cDNA microarray data and to a set of cancerous/normal sample data from colon cancer patients. We discuss extending our methods to inferring biological pathways and networks using more complex dynamic Bayesian networks.
Download or read book A Bayesian Large Scale Multiple Regression Model for Genome Wide Association Summary Statistics written by Xiang Zhu and published by . This book was released on 2017 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: We apply RSS methods to analyze published GWAS summary statistics of 1.1 millions common variants from 31 human phenotypes, 3,913 biological pathways retrieved from nine public databases, and 113 tissue-associated gene sets derived from gene expression profiles of 53 human tissues. We identify many previously-unreported genes, pathways and tissues that show strong evidence for association with complex traits in our large-scale integrated analyses. Software is available at https://github.com/stephenslab/rss.
Download or read book Applied Statistics for Network Biology written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2011-04-08 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed as a valuable guide for the experimental researcher as well computational biologists who often lack an appropriate background in statistical analysis.
Download or read book Protein protein Interactions and Networks written by Anna Panchenko and published by Springer Science & Business Media. This book was released on 2010-04-06 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: The biological interactions of living organisms, and protein-protein interactions in particular, are astonishingly diverse. This comprehensive book provides a broad, thorough and multidisciplinary coverage of its field. It integrates different approaches from bioinformatics, biochemistry, computational analysis and systems biology to offer the reader a comprehensive global view of the diverse data on protein-protein interactions and protein interaction networks.
Download or read book Analysis of Gene Expression Microarray Time Series Data written by Ola ElBakry and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Systems Genetics written by Florian Markowetz and published by Cambridge University Press. This book was released on 2015-07-02 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whereas genetic studies have traditionally focused on explaining heritance of single traits and their phenotypes, recent technological advances have made it possible to comprehensively dissect the genetic architecture of complex traits and quantify how genes interact to shape phenotypes. This exciting new area has been termed systems genetics and is born out of a synthesis of multiple fields, integrating a range of approaches and exploiting our increased ability to obtain quantitative and detailed measurements on a broad spectrum of phenotypes. Gathering the contributions of leading scientists, both computational and experimental, this book shows how experimental perturbations can help us to understand the link between genotype and phenotype. A snapshot of current research activity and state-of-the-art approaches to systems genetics are provided, including work from model organisms such as Saccharomyces cerevisiae and Drosophila melanogaster, as well as from human studies.
Download or read book Statistical Problems in DNA Microarray Data Analysis written by Nancy Naichao Wang and published by . This book was released on 2009 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: DNA microarrays are powerful tools for functional genomics studies. Each array contains thousands of microscopic spots of DNA oligonucleotides with specific sequences, which can hybridize with their complementary DNA sequences. Thus each microarray experiment consists of parallel assays about thousands of genomic fragments. This thesis concerns some statistical issues in the analysis of DNA microarray data. One common usage of DNA microarrays is to monitor the dynamic levels of gene expression in response to a stimulus. This is often achieved through a time course experiment, in which RNA samples are extracted at various time points after exposing the organism to the stimulus. A particularly interesting type of time course experiments involve replicated series of longitudinal samples. In 2006, Tai and Speed proposed a multivariate empirical Bayes model for analyzing this type of data. The MB-statistic derived from this model was shown useful for ranking the genes according to changes in their temporal expression profiles. In the first part of this thesis, we propose an empirical Bayes false discovery rate (FDR)-controlling procedure for multiple hypothesis testing using the MB-statistic. A null distribution is obtained using the parametric bootstrap. Critical values are determined according to the empirical Bayes FDR procedure. This method was compared, through simulations, to the frequentist FDR procedure, which requires a theoretical null distribution for calculating the nominal p-values. Although our method is slightly anti-conservative, it is more robust to the variability in the estimates of the hyperparameters, when the degree of moderation is small. Another common usage of DNA microarrays is to detect genomic locations that are associated with DNA-binding proteins. This is often achieved through ChIP-chip experiments that combine chromatin immunoprecipitation with the microarray technology. Traditional DNA microarrays designed for gene expression studies contain only a few probes for each gene. A special type of DNA microarrays, called tiling arrays, are often used in ChIP-chip experiments. They typically contain probes that are placed densely along the chromosomes to cover either the entire genome or contigs of the genome. A couple of challenges in the analysis of ChIP-chip tiling array data have not been met satisfactorily in the literature. When large scale genomic studies are carried over a long period of time, tiling arrays with different probe designs are often used for practical reasons. The first challenge is the integration of replicate experiments performed using different tiling array designs. When the biological process of interest involves a large protein complex, the investigators often perform ChIP-chip experiments on each component DNA-binding protein individually. DNA targets that are shared by the individual proteins are thought to be the localization sites of the protein complex. The second challenge is the joint analysis of multiple DNA-binding proteins, aimed at identifying their shared targets. In the second part of this thesis, we propose a nonhomogeneous hidden Markov model (HMM) for addressing these two challenges. The nonhomogeneous time axis represents the genomic positions of the probes. The hidden states represent the binding statuses of the proteins. The state-conditional emission distributions of the tiling array data are protein-specific and design-specific. We derived a modified Baum-Welch algorithm for fitting the model parameters. We also developed a procedure that converts the probe level summaries into peaks, which represent the putative binding sites, based on both signal strength and peak shape. To compare our method with existing methods, we curated a set of positive and negative genomic regions from a C. elegans dataset, and performed some receiver operating characteristics (ROC) analyses. When applied to each experiment separately, our method performs similarly as the three best existing methods. When applied to the combined data set, which consists of tiling arrays with different probe designs, our method shows a drastic improvement in performance. A generalization of the nonhomogeneous HMM enables the joint analysis of the ChIP-chip data of multiple proteins. We present an application of this method to identify the shared localization sites of two DNA-binding proteins, under two different conditions.
Download or read book Machine Learning for Large scale Genomics written by Yifei Chen and published by . This book was released on 2014 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic malformations are believed to be the driving factors of many diseases. Therefore, understanding the intrinsic mechanisms underlying the genome and informing clinical practices have become two important missions of large-scale genomic research. Recently, high-throughput molecular data have provided abundant information about the whole genome, and have popularized computational tools in genomics. However, traditional machine learning methodologies often suffer from strong limitations when dealing with high-throughput genomic data, because the latter are usually very high dimensional, highly heterogeneous, and can show complicated nonlinear effects. In this thesis, we present five new algorithms or models to address these challenges, each of which is applied to a specific genomic problem. Project 1 focuses on model selection in cancer diagnosis. We develop an efficient algorithm (ADMM-ENSVM) for the Elastic Net Support Vector Machine, which achieves simultaneous variable selection and max-margin classification. On a colon cancer diagnosis dataset, ADMM-ENSVM shows advantages over other SVM algorithms in terms of diagnostic accuracy, feature selection ability, and computational efficiency. Project 2 focuses on model selection in gene correlation analysis. We develop an efficient algorithm (SBLVGG) using the similar methodology as of ADMM-ENSVM for the Latent Variable Gaussian Graphical Model (LVGG). LVGG models the marginal concentration matrix of observed variables as a combination of a sparse matrix and a low rank one. Evaluated on a microarray dataset containing 6,316 genes, SBLVGG is notably faster than the state-of-the-art LVGG solver, and shows that most of the correlation among genes can be effectively explained by only tens of latent factors. Project 3 focuses on ensemble learning in cancer survival analysis. We develop a gradient boosting model (GBMCI), which does not explicitly assume particular forms of hazard functions, but trains an ensemble of regression trees to approximately optimize the concordance index. We benchmark the performance of GBMCI against several popular survival models on a large-scale breast cancer prognosis dataset. GBMCI consistently outperforms other methods based on a number of feature representations, which are heterogeneous and contain missing values. Project 4 focuses on deep learning in gene expression inference (GEIDN). GEIDN is a large-scale neural network, which can infer ~21k target genes jointly from ~1k landmark genes and can naturally capture hierarchical nonlinear interactions among genes. We deploy deep learning techniques (drop out, momentum training, GPU computing, etc.) to train GEIDN. On a dataset of ~129k complete human transcriptomes, GEIDN outperforms both k-nearest neighbor regression and linear regression in predicting >99.96% of the target genes. Moreover, increased network scales help to improve GEIDN, while increased training data benefits GEIDN more than other methods. Project 5 focuses on deep learning in annotating coding and noncoding genetic variants (DANN). DANN is a neural network to differentiate evolutionarily derived alleles from simulated ones with 949 highly heterogeneous features. It can capture nonlinear relationships among features. We train DANN with deep learning techniques like for GEIDN. DANN achieves a 18.90% relative reduction in the error rate and a 14.52% relative increase in the area under the curve over CADD, a state-of-the-art algorithm to annotate genetic variants based on the linear SVM.
Download or read book Big and Complex Data Analysis written by S. Ejaz Ahmed and published by Springer. This book was released on 2017-03-21 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.
Download or read book Biocomputing written by Panos M. Pardalos and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the quest to understand and model the healthy or sick human body, re searchers and medical doctors are utilizing more and more quantitative tools and techniques. This trend is pushing the envelope of a new field we call Biomedical Computing, as an exciting frontier among signal processing, pattern recognition, optimization, nonlinear dynamics, computer science and biology, chemistry and medicine. A conference on Biocomputing was held during February 25-27, 2001 at the University of Florida. The conference was sponsored by the Center for Applied Optimization, the Computational Neuroengineering Center, the Biomedical En gineering Program (through a Whitaker Foundation grant), the Brain Institute, the School of Engineering, and the University of Florida Research & Graduate Programs. The conference provided a forum for researchers to discuss and present new directions in Biocomputing. The well-attended three days event was highlighted by the presence of top researchers in the field who presented their work in Biocomputing. This volume contains a selective collection of ref ereed papers based on talks presented at this conference. You will find seminal contributions in genomics, global optimization, computational neuroscience, FMRI, brain dynamics, epileptic seizure prediction and cancer diagnostics. We would like to take the opportunity to thank the sponsors, the authors of the papers, the anonymous referees, and Kluwer Academic Publishers for making the conference successful and the publication of this volume possible. Panos M. Pardalos and Jose C.
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 written by Marco Scutari and published by CRC Press. This book was released on 2021-07-28 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the material step-by-step starting from meaningful examples Steps detailed with R code in the spirit of reproducible research Real world data analyses from a Science paper reproduced and explained in detail Examples span a variety of fields across social and life sciences Overview of available software in and outside R
