Download or read book An Ensemble Learning Approach to Reverse engineering Transcriptional Regulatory Networks from Time series Gene Expression Data written by and published by . This book was released on 2009 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Reverse Engineering of Regulatory Networks written by Sudip Mandal and published by Springer Nature. This book was released on 2023-11-07 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume details the development of updated dry lab and wet lab based methods for the reconstruction of Gene regulatory networks (GRN). Chapters guide readers through culprit genes, in-silico drug discovery techniques, genome-wide ChIP-X data, high-Throughput Transcriptomic Data Exome Sequencing, Next-Generation Sequencing, Fuorescence Spectroscopy, data analysis in Bioinformatics, Computational Biology, and S-system based modeling of GRN. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Reverse Engineering of Regulatory Networks aims to be a useful and practical guide to new researchers and experts looking to expand their knowledge.
Download or read book Inferring Gene Regulatory Networks from Expression Data Using Ensemble Methods written by Janusz Slawek and published by . This book was released on 2014 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput technologies for measuring gene expression made inferring of the genome-wide Gene Regulatory Networks an active field of research. Reverse-engineering of systems of transcriptional regulations became an important challenge in molecular and computational biology. Because such systems model dependencies between genes, they are important in understanding of cell behavior, and can potentially turn observed expression data into the new biological knowledge and practical applications. In this dissertation we introduce a set of algorithms, which infer networks of transcriptional regulations from variety of expression profiles with superior accuracy compared to the state-of-the-art techniques. The proposed methods make use of ensembles of trees, which became popular in many scientific fields, including genetics and bioinformatics. However, originally they were motivated from the perspective of classification, regression, and feature selection theory. In this study we exploit their relative variable importance measure as an indication of the presence or absence of a regulatory interaction between genes. We further analyze their predictions on a set of the universally recognized benchmark expression data sets, and achieve favorable results in compare with the state-of-the-art algorithms.
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 Systems Biology of Transcription Regulation written by Ekaterina Shelest and published by Frontiers Media SA. This book was released on 2016-09-09 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transcription regulation is a complex process that can be considered and investigated from different perspectives. Traditionally and due to technical reasons (including the evolution of our understanding of the underlying processes) the main focus of the research was made on the regulation of expression through transcription factors (TFs), the proteins directly binding to DNA. On the other hand, intensive research is going on in the field of chromatin structure, remodeling and its involvement in the regulation. Whatever direction we select, we can speak about several levels of regulation. For instance, concentrating on TFs, we should consider multiple regulatory layers, starting with signaling pathways and ending up with the TF binding sites in the promoters and other regulatory regions. However, it is obvious that the TF regulation, also including the upstream processes, represents a modest portion of all processes leading to gene expression. For more comprehensive description of the gene regulation, we need a systematic and holistic view, which brings us to the importance of systems biology approaches. Advances in methodology, especially in high-throughput methods, result in an ever-growing mass of data, which in many cases is still waiting for appropriate consideration. Moreover, the accumulation of data is going faster than the development of algorithms for their systematic evaluation. Data and methods integration is indispensable for the acquiring a systematic as well as a systemic view. In addition to the huge amount of molecular or genetic components of a biological system, the even larger number of their interactions constitutes the enormous complexity of processes occurring in a living cell (organ, organism). In systems biology, these interactions are represented by networks. Transcriptional or, more generally, gene regulatory networks are being generated from experimental ChIPseq data, by reverse engineering from transcriptomics data, or from computational predictions of transcription factor (TF) – target gene relations. While transcriptional networks are now available for many biological systems, mathematical models to simulate their dynamic behavior have been successfully developed for metabolic and, to some extent, for signaling networks, but relatively rarely for gene regulatory networks. Systems biology approaches provide new perspectives that raise new questions. Some of them address methodological problems, others arise from the newly obtained understanding of the data. These open questions and problems are also a subject of this Research Topic.
Download or read book Evolutionary Computation in Gene Regulatory Network Research written by Hitoshi Iba and published by John Wiley & Sons. This book was released on 2016-01-20 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. • Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) • Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications • Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology • Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
Download or read book Expression Based Reverse Engineering of Plant Transcriptional Networks written by Federico Giorgi and published by LAP Lambert Academic Publishing. This book was released on 2012-03 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regulation of gene transcription plays a major role in mediating cellular responses and physiological behavior in all known organisms. The finding that similar genes are often regulated in a similar manner (co-regulated or "co-expressed") has directed several "guilt-by-association" approaches in order to reverse-engineer the cellular transcriptional networks using gene expression data as a compass. This kind of studies has been considerably assisted in the recent years by the development of high-throughput transcript measurement platforms, specifically gene microarrays and next-generation sequencing. In this thesis, I describe several approaches for improving the extraction and interpretation of the information contained in microarray based gene expression data, through four steps: (1) microarray platform design, (2) microarray data normalization, (3) gene network reverse engineering based on expression data and (4) experimental validation of expression-based guilt-by-association inferences. In the first part test case is shown aimed at the generation of a microarray for Thellungiella salsuginea, a salt and drought resistant close relative to the model plant Arabidopsis thaliana; the transcripts of this organism are generated on the combination of publicly available ESTs and newly generated ad-hoc next-generation sequencing data. Since the design of a microarray platform requires the availability of highly reliable and non-redundant transcript models, these issues are addressed consecutively, proposing several different technical solutions. In the second part I describe how inter-array correlation artifacts are generated by the common microarray normalization methods RMA and GCRMA, together with the technical and mathematical characteristics underlying the problem. A solution is proposed in the form of a novel normalization method, called tRMA. The third part of the thesis deals with the field of expression-based gene network reverse engineering. It is shown how different centrality measures in reverse engineered gene networks can be used to distinguish specific classes of genes, in particular essential genes in Arabidopsis thaliana, and how the use of conditional correlation can add a layer of understanding over the information flow processes underlying transcript regulation. Furthermore, several network reverse engineering approaches are compared, with a particular focus on the LASSO, a linear regression derivative rarely applied before in global gene network reconstruction, despite its theoretical advantages in robustness and interpretability over more standard methods. The performance of LASSO is assessed through several in silico analyses dealing with the reliability of the inferred gene networks. In the final part, LASSO and other reverse engineering methods are used to experimentally identify novel genes involved in two independent scenarios: the seed coat mucilage pathway in Arabidopsis thaliana and the hypoxic tuber development in Solanum tuberosum. In both cases an interesting method complementarity is shown, which strongly suggests a general use of hybrid approaches for transcript expression-based inferences.In conclusion, this work has helped to improve our understanding of gene transcription regulation through a better interpretation of high-throughput expression data. Part of the network reverse engineering methods described in this thesis have been included in a tool (CorTo) for gene network reverse engineering and annotated visualization from custom transcription datasets.
Download or read book Machine Learning Methods in Construction of Transcriptional Regulatory Networks written by Yue Fan and published by . This book was released on 2012 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The transcriptional regulatory network is a biological network that captures the interactions between transcription factor genes (TF-genes) and their regulatory gene targets. Regulation of transcription controls the level of gene expression and thus governs many characteristics of cells. The primary mechanism of transcriptional regulation is through DNA binding, that is, a transcription factor is usually bound to a DNA binding site which is sometimes located in the promoter region of a target gene. The construction of the regulatory network is a problem which can be decomposed into the sub-problems of identifying, for every known gene which produces a TF, its target genes, its binding motif (common sequence pattern of its DNA binding sites) and its DNA binding sites themselves (nucleotide-level binding locations). Many tools have been developed in the last decade to solve these problems. This thesis presents a series of machine learning-based algorithms, making use of support vector machines (SVMs), which can be used to construct the transcriptional regulatory network. This has also established a framework which enables other machine learning algorithms to be applied to this field. The connection between new machine learning methods and traditional methods for solving the above problems also suggests that the machine methods introduced have the potential to identify optimal solutions based on the use training examples of binding motifs, binding sites, and target genes of a given TF. Based on the insights of a pilot project (TFSVM), we first develop a motif discovery tool (SVMotif) to discover binding motifs out of a set of pre-identified potential binding sequences. This tool, tested on the yeast genome, validates many previously identified motifs and also discovers novel ones. Besides identifying primary binding motifs, this tool also successfully identifies 20 secondary motifs at the p = 0.15 significance level. In order to leverage the advantage of different motif discovery algorithms, an ensemble algorithm is then developed to integrate information from multiple position weight matrices (PWM) produced by 5 commonly used motif discovery algorithms. A connection between the SVM-based methods and traditional PWM-based methods is described, which becomes the basis of integrating multiple PWMs by considering them as SVM-based weak learners. This ensemble method is tested in solving the three above-mentioned identification problems--it outperforms its 5 components on all tasks. Finally, a machine framework is proposed and implemented to utilize network information to denoise gene expression feature vectors used for diagnosis and prognosis in biological and biomedical problems. Several local smoothing techniques from statistics are generalized to the graphs/networks obtained from the above and other network construction methods. We then applied the algorithm to denoising gene expression profiles--the resulting smoothed gene expression features improve the accuracy of biological phenotype classification significantly.
Download or read book Transcription Factor Regulatory Networks written by Qi Song and published by Springer Nature. This book was released on 2022-10-20 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers various state-of-the-art techniques regarding the associations between transcription factors (TFs) and genes, with a focus on providing methodological and practical references for researchers. The contents cover diverse protocols and summaries of TFs including screening of TF-DNA interactions, detection of open chromatin regions, identification of epigenetic regulations, engineering TFs with genome editing tools, detection of transcriptional activities, computational analysis of TF networks, functions and druggabilities of TFs in biomedical research, and much more. Written for the highly successful Methods in Molecular Biology series, chapters feature the kind of detailed implementation advice from the experts to ensure successful research results. Authoritative and cutting-edge, Transcription Factor Regulatory Networks aims to benefit readers who are interested in using state-of-the-art techniques to study TFs and their myriad effects in cellular life.
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 The International Conference on Advanced Machine Learning Technologies and Applications AMLTA2019 written by Aboul Ella Hassanien and published by Springer. This book was released on 2019-03-16 with total page 960 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the peer-reviewed proceedings of the 4th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2019), held in Cairo, Egypt, on March 28–30, 2019, and organized by the Scientific Research Group in Egypt (SRGE). The papers cover the latest research on machine learning, deep learning, biomedical engineering, control and chaotic systems, text mining, summarization and language identification, machine learning in image processing, renewable energy, cyber security, and intelligence swarms and optimization.
Download or read book Reverse Engineering Biological Networks written by Gustavo Stolovitzky and published by Wiley-Blackwell. This book was released on 2007-12-26 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This volume is the result of a workshop entitled Dialogue on Reverse Engineering Assessment and Methods (DREAM) held on September 7-8, 2006, at Wave Hill, New York"--P [vii].
Download or read book Emerging Research in the Analysis and Modeling of Gene Regulatory Networks written by Ivanov, Ivan V. and published by IGI Global. This book was released on 2016-06-06 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: While technological advancements have been critical in allowing researchers to obtain more and better quality data about cellular processes and signals, the design and practical application of computational models of genomic regulation continues to be a challenge. Emerging Research in the Analysis and Modeling of Gene Regulatory Networks presents a compilation of recent and emerging research topics addressing the design and use of technology in the study and simulation of genomic regulation. Exploring both theoretical and practical topics, this publication is an essential reference source for students, professionals, and researchers working in the fields of genomics, molecular biology, bioinformatics, and drug development.
Download or read book Ubiquity written by Arie Hasman and published by IOS Press. This book was released on 2006 with total page 1070 pages. Available in PDF, EPUB and Kindle. Book excerpt: Talks about the ubiquitous computing that helps us to identify ways of managing care that promises to be considerably easier in letting patients maintain their good health while enjoying their life in their usual social setting, rather than having to spend much time at costly, dedicated healthcare facilities.
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 Machine Learning and Knowledge Discovery in Databases written by José L. Balcázar and published by Springer. This book was released on 2010-08-17 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation. This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2010, held in Barcelona, Spain, in September 2010. The 120 revised full papers presented in three volumes, together with 12 demos (out of 24 submitted demos), were carefully reviewed and selected from 658 paper submissions. In addition, 7 ML and 7 DM papers were distinguished by the program chairs on the basis of their exceptional scientific quality and high impact on the field. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. A topic widely explored from both ML and DM perspectives was graphs, with motivations ranging from molecular chemistry to social networks.
