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A Novel Computational Framework For Fasts Distributed Computing And Knowledge Integration For Microarray Gene Expression Data Analysis
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Book Synopsis A Novel Computational Framework for Fasts, Distributed Computing and Knowledge Integration for Microarray Gene Expression Data Analysis by : Prerna Sethi
Download or read book A Novel Computational Framework for Fasts, Distributed Computing and Knowledge Integration for Microarray Gene Expression Data Analysis written by Prerna Sethi and published by . This book was released on 2006 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Gene Expression Data Analysis by : Pankaj Barah
Download or read book Gene Expression Data Analysis written by Pankaj Barah and published by CRC Press. This book was released on 2021-11-08 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2006 with total page 846 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela
Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by MDPI. This book was released on 2021-02-05 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Book Synopsis A Study of Computational Methods to Analyze Gene Expression Data by : Youn Hee Ko
Download or read book A Study of Computational Methods to Analyze Gene Expression Data written by Youn Hee Ko and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent advent of new technologies has led to huge amounts of genomic data. With these data come new opportunities to understand biological cellular processes underlying hidden regulation mechanisms and to identify disease related biomarkers for informative diagnostics. However, extracting biological insights from the immense amounts of genomic data is a challenging task. Therefore, effective and efficient computational techniques are needed to analyze and interpret genomic data. In this thesis, novel computational methods are proposed to address such challenges: a Bayesian mixture model, an extended Bayesian mixture model, and an Eigen-brain approach. The Bayesian mixture framework involves integration of the Bayesian network and the Gaussian mixture model. Based on the proposed framework and its conjunction with K-means clustering and principal component analysis (PCA), biological insights are derived such as context specific/dependent relationships and nested structures within microarray where biological replicates are encapsulated. The Bayesian mixture framework is then extended to explore posterior distributions of network space by incorporating a Markov chain Monte Carlo (MCMC) model. The extended Bayesian mixture model summarizes the sampled network structures by extracting biologically meaningful features. Finally, an Eigen-brain approach is proposed to analyze in situ hybridization data for the identification of the cell-type specific genes, which can be useful for informative blood diagnostics. Computational results with region-based clustering reveals the critical evidence for the consistency with brain anatomical structure.
Book Synopsis Handbook of Research on Computational Methodologies in Gene Regulatory Networks by : Das, Sanjoy
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.
Book Synopsis Development of an Extensible Computational Framework for Centralized Storage and Distributed Curation and Analysis of Genomic Data Genome-scale Metabolic Models by :
Download or read book Development of an Extensible Computational Framework for Centralized Storage and Distributed Curation and Analysis of Genomic Data Genome-scale Metabolic Models written by and published by . This book was released on 2010 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt: The DOE funded KBase project of the Stevens group at the University of Chicago was focused on four high-level goals: (i) improve extensibility, accessibility, and scalability of the SEED framework for genome annotation, curation, and analysis; (ii) extend the SEED infrastructure to support transcription regulatory network reconstructions (2.1), metabolic model reconstruction and analysis (2.2), assertions linked to data (2.3), eukaryotic annotation (2.4), and growth phenotype prediction (2.5); (iii) develop a web-API for programmatic remote access to SEED data and services; and (iv) application of all tools to bioenergy-related genomes and organisms. In response to these goals, we enhanced and improved the ModelSEED resource within the SEED to enable new modeling analyses, including improved model reconstruction and phenotype simulation. We also constructed a new website and web-API for the ModelSEED. Further, we constructed a comprehensive web-API for the SEED as a whole. We also made significant strides in building infrastructure in the SEED to support the reconstruction of transcriptional regulatory networks by developing a pipeline to identify sets of consistently expressed genes based on gene expression data. We applied this pipeline to 29 organisms, computing regulons which were subsequently stored in the SEED database and made available on the SEED website (http://pubseed.theseed.org). We developed a new pipeline and database for the use of kmers, or short 8-residue oligomer sequences, to annotate genomes at high speed. Finally, we developed the PlantSEED, or a new pipeline for annotating primary metabolism in plant genomes. All of the work performed within this project formed the early building blocks for the current DOE Knowledgebase system, and the kmer annotation pipeline, plant annotation pipeline, and modeling tools are all still in use in KBase today.
Book Synopsis A Computational Framework for the Analysis of Multi-species Microarray Data by : Yong Lu
Download or read book A Computational Framework for the Analysis of Multi-species Microarray Data written by Yong Lu and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Methods for Analyzing and Modeling Gene Regulation and 3D Genome Organization by : Anastasiya Belyaeva
Download or read book Computational Methods for Analyzing and Modeling Gene Regulation and 3D Genome Organization written by Anastasiya Belyaeva and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Biological processes from differentiation to disease progression are governed by gene regulatory mechanisms. Currently large-scale omics and imaging data sets are being collected to characterize gene regulation at every level. Such data sets present new opportunities and challenges for extracting biological insights and elucidating the gene regulatory logic of cells. In this thesis, I present computational methods for the analysis and integration of various data types used for cell profiling. Specifically, I focus on analyzing and linking gene expression with the 3D organization of the genome. First, I describe methodologies for elucidating gene regulatory mechanisms by considering multiple data modalities. I design a computational framework for identifying colocalized and coregulated chromosome regions by integrating gene expression and epigenetic marks with 3D interactions using network analysis. Then, I provide a general framework for data integration using autoencoders and apply it for the integration and translation between gene expression and chromatin images of naive T-cells. Second, I describe methods for analyzing single modalities such as contact frequency data, which measures the spatial organization of the genome, and gene expression data. Given the important role of the 3D genome organization in gene regulation, I present a methodology for reconstructing the 3D diploid conformation of the genome from contact frequency data. Given the ubiquity of gene expression data and the recent advances in single-cell RNA-sequencing technologies as well as the need for causal modeling of gene regulatory mechanisms, I then describe an algorithm as well as a software tool, difference causal inference (DCI), for learning causal gene regulatory networks from gene expression data. DCI addresses the problem of directly learning differences between causal gene regulatory networks given gene expression data from two related conditions. Finally, I shift my focus from basic biology to drug discovery. Given the current COVID19 pandemic, I present a computational drug repurposing platform that enables the identification of FDA approved compounds for drug repurposing and investigation of potential causal drug mechanisms. This framework relies on identifying drugs that reverse the signature of the infection in the space learned by an autoencoder and then uses causal inference to identify putative drug mechanisms.
Book Synopsis Computational Analysis of Gene Expression Data by : Alexander Zien
Download or read book Computational Analysis of Gene Expression Data written by Alexander Zien and published by . This book was released on 2004 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Novel Computational Framework for Transcriptome Analysis with RNA-seq Data by : Yin Hu
Download or read book A Novel Computational Framework for Transcriptome Analysis with RNA-seq Data written by Yin Hu and published by . This book was released on 2013 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela
Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by . This book was released on 2021 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Book Synopsis Computational Methods for Knowledge Integration in the Analysis of Large-scale Biological Networks by : Archana Ramesh
Download or read book Computational Methods for Knowledge Integration in the Analysis of Large-scale Biological Networks written by Archana Ramesh and published by . This book was released on 2012 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation develops a framework of computational methods for the analysis of such data-driven hypotheses leveraging existing biological knowledge. Specifically, I show how biological knowledge can be mapped onto these hypotheses and subsequently augmented through novel hypotheses. Biological hypotheses are learnt in three levels of abstraction -- individual interactions, functional modules and relationships between pathways, corresponding to three complementary aspects of biological systems. The computational methods developed in this dissertation are applied to high throughput cancer data, resulting in novel hypotheses with potentially significant biological impact.
Book Synopsis Prior Knowledge Integration of Gene Networks Data Into Gene Expression Analysis by : Ofer Lavi
Download or read book Prior Knowledge Integration of Gene Networks Data Into Gene Expression Analysis written by Ofer Lavi and published by . This book was released on 2010 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Methods to Elucidate Post-transcriptional Gene Regulation Using High-throughput Sequencing Data by : Zijun Zhang
Download or read book Computational Methods to Elucidate Post-transcriptional Gene Regulation Using High-throughput Sequencing Data written by Zijun Zhang and published by . This book was released on 2019 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Post-transcriptional regulation plays a central role in the flow of information from genotypes to phenotypes in the cellular machinery. Disruptions of post-transcriptional regulatory mechanisms underlie many human diseases. As high-throughput sequencing technology becomes the standard protocol in studying post-transcriptional regulation, large-scale data in public domain provides an unprecedented resource to understand the complex regulatory networks of gene regulation, while also presents challenges for the development of computational methods to analyze and interpret empirical data into biological knowledge. In this dissertation, novel statistical models and computational frameworks were developed to elucidate post-transcriptional gene regulation using high-throughput sequencing data. Utilizing these new tools, we demonstrated that we can robustly characterize the molecular signals and variations across diverse biological states, and more importantly, identify bona fide regulatory events that are inaccessible by conventional analyses. The first part of the dissertation describes CLIP-seq Analysis of Multi-mapped reads (CLAM), a comprehensive computational pipeline for analyzing Crosslinking or RNA immunoprecipitation followed by sequencing (CLIP/RIP-seq) data. As CLIP-seq/RIP-seq reads are short, existing computational tools focus on uniquely mapped reads, while reads mapped to multiple loci are discarded. CLAM uses an expectation-maximization algorithm to assign multi-mapped reads and calls peaks combining uniquely and multi-mapped reads. CLAM recovered a large number of novel RNA regulatory sites inaccessible by uniquely mapped reads in datasets with different regulatory features, providing a useful tool to discover novel protein-RNA interactions and RNA modification sites from CLIP-seq and RIP-seq data. The second part of the dissertation presents Deep-learning Augmented RNA-seq analysis of Transcript Splicing (DARTS), a novel computational framework that integrates deep learning-based predictions with empirical RNA-seq datasets to infer differential alternative splicing between biological conditions. A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. DARTS employs a deep neural network (DNN) that predicts differential alternative splicing using cis RNA sequence features and trans RNA binding protein levels. DARTS DNN trained on public RNA-seq displays a high prediction accuracy and generalizability. Incorporating DARTS DNN prediction as an informative prior significantly improves the inference of differential alternative splicing. DARTS leverages public RNA-seq big data to provide a knowledge base of splicing regulation via deep learning, thereby helping researchers better characterize alternative splicing using RNA-seq datasets even with modest coverage.
Book Synopsis ALGORITHMS FOR RECONSTRUCTION OF GENE REGULATORY NETWORKS FROM HIGH -THROUGHPUT GENE EXPRESSION DATA by :
Download or read book ALGORITHMS FOR RECONSTRUCTION OF GENE REGULATORY NETWORKS FROM HIGH -THROUGHPUT GENE EXPRESSION DATA written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : Understanding gene interactions in complex living systems is one of the central tasks in system biology. With the availability of microarray and RNA-Seq technologies, a multitude of gene expression datasets has been generated towards novel biological knowledge discovery through statistical analysis and reconstruction of gene regulatory networks (GRN). Reconstruction of GRNs can reveal the interrelationships among genes and identify the hierarchies of genes and hubs in networks. The new algorithms I developed in this dissertation are specifically focused on the reconstruction of GRNs with increased accuracy from microarray and RNA-Seq high-throughput gene expression data sets. The first algorithm (Chapter 2) focuses on modeling the transcriptional regulatory relationships between transcription factors (TF) and pathway genes. Multiple linear regression and its regularized version, such as Ridge regression and LASSO, are common tools that are usually used to model the relationship between predictor variables and dependent variable. To deal with the outliers in gene expression data, the group effect of TFs in regulation and to improve the statistical efficiency, it is proposed to use Huber function as loss function and Berhu function as penalty function to model the relationships between a pathway gene and many or all TFs. A proximal gradient descent algorithm was developed to solve the corresponding optimization problem. This algorithm is much faster than the general convex optimization solver CVX. Then this Huber-Berhu regression was embedded into partial least square (PLS) framework to deal with the high dimension and multicollinearity property of gene expression data. The result showed this method can identify the true regulatory TFs for each pathway gene with high efficiency. The second algorithm (Chapter 3) focuses on building multilayered hierarchical gene regulatory networks (ML-hGRNs). A backward elimination random forest (BWERF) algorithm was developed for constructing an ML-hGRN operating above a biological pathway or a biological process. The algorithm first divided construction of ML-hGRN into multiple regression tasks; each involves a regression between a pathway gene and all TFs. Random forest models with backward elimination were used to determine the importance of each TF to a pathway gene. Then the importance of a TF to the whole pathway was computed by aggregating all the importance values of the TF to the individual pathway gene. Next, an expectation maximization algorithm was used to cut the TFs to form the first layer of direct regulatory relationships. The upper layers of GRN were constructed in the same way only replacing the pathway genes by the newly cut TFs. Both simulated and real gene expression data were used to test the algorithms and demonstrated the accuracy and efficiency of the method. The third algorithm (Chapter 4) focuses on Joint Reconstruction of Multiple Gene Regulatory Networks (JRmGRN) using gene expression data from multiple tissues or conditions. In the formulation, shared hub genes across different tissues or conditions were assumed. Under the framework of the Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log-likelihood function. It was formulated as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. Both simulated and real gene expression data manifested JRmGRN had better performance than existing methods.
Book Synopsis Computational Methods for Analyzing and Modeling Gene Regulation Dynamics by : Jason Ernst
Download or read book Computational Methods for Analyzing and Modeling Gene Regulation Dynamics written by Jason Ernst and published by . This book was released on 2008 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Gene regulation is a central biological process whose disruption can lead to many diseases. This process is largely controlled by a dynamic network of transcription factors interacting with specific genes to control their expression. Time series microarray gene expression experiments have become a widely used technique to study the dynamics of this process. This thesis introduces new computational methods designed to better utilize data from these experiments and to integrate this data with static transcription factor-gene interaction data to analyze and model the dynamics of gene regulation. The first method, STEM (Short Time-series Expression Miner), is a clustering algorithm and software specifically designed for short time series expression experiments, which represent the substantial majority of experiments in this domain. The second method, DREM (Dynamic Regulatory Events Miner), integrates transcription factor-gene interactions with time series expression data to model regulatory networks while taking into account their dynamic nature. The method uses an Input-Output Hidden Markov Model to identify bifurcation points in the time series expression data. While the method can be readily applied to some species, the coverage of experimentally determined transcription factor-gene interactions in most species is limited. To address this we introduce two methods to improve the computational predictions of these interactions. The first of these methods, SEREND (SEmi-supervised REgulatory Network Discoverer), motivated by the species E. coli is a semi-supervised learning method that uses verified transcription factor-gene interactions, DNA sequence binding motifs, and gene expression data to predict new interactions. We also present a method motivated by human genomic data, that combines motif information with a probabilistic prior on transcription factor binding at each location in the organism's genome, which it infers based on a diverse set of genomic properties. We applied these methods to yeast, E. coli, and human cells. Our methods successfully predicted interactions and pathways, many of which have been experimentally validated. Our results indicate that by explicitly addressing the temporal nature of regulatory networks we can obtain accurate models of dynamic interaction networks in the cell."
