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Author: Usha Kasid
Publisher: Methods in Molecular Biology
ISBN: 9781493982301
Category : Medical
Languages : en
Pages : 262
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Book Description
This volume is a valuable and timely resource for a broad audience with interests in basic and translational cancer biology, cancer drug development, as well as in the practice of personalized oncology. 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 tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Cancer Gene Networks aims to ensure successful results in the further study of this evolving and vital field. Ultimately these efforts will guide development of transformative strategies for cancer diagnosis and treatment.
Author: Usha Kasid
Publisher: Methods in Molecular Biology
ISBN: 9781493982301
Category : Medical
Languages : en
Pages : 262
Get Book Here
Book Description
This volume is a valuable and timely resource for a broad audience with interests in basic and translational cancer biology, cancer drug development, as well as in the practice of personalized oncology. 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 tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Cancer Gene Networks aims to ensure successful results in the further study of this evolving and vital field. Ultimately these efforts will guide development of transformative strategies for cancer diagnosis and treatment.
Author: Alberto Fuente
Publisher: Springer Science & Business Media
ISBN: 3642451616
Category : Science
Languages : en
Pages : 135
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Book Description
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.
Author: Shruti Mishra
Publisher: Academic Press
ISBN: 0128163577
Category : Science
Languages : en
Pages : 133
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Book Description
Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome helps readers identify and select the specific genes causing oncogenes. The book also addresses the validation of the selected genes using various classification techniques and performance metrics, making it a valuable source for cancer researchers, bioinformaticians, and researchers from diverse fields interested in applying systems biology approaches to their studies. Provides well described techniques for the purpose of gene selection/feature selection for the generation of gene subsets Presents and analyzes three different types of gene selection algorithms: Support Vector Machine-Bayesian T-Test-Recursive Feature Elimination (SVM-BT-RFE), Canonical Correlation Analysis-Trace Ratio (CCA-TR), and Signal-To-Noise Ratio-Trace Ratio (SNRTR) Consolidates fundamental knowledge on gene datasets and current techniques on gene regulatory networks into a single resource
Author: Paul Scott
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 54
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Book Description
Author's abstract: Cancer prevention treatments are being researched to see if an optimized treatment schedule would decrease the likelihood of a person being diagnosed with cancer. To do this we are looking at genes involved in the cell cycle and how they interact with one another. Through each gene expression during the life of a normal cell we get an understanding of the gene interactions and test these against those of a cancerous cell. First we construct a simplified network model of the normal gene network. Once we have this model we translate it into a transition matrix and force changes on it. Observing the effects of the changes we see the interactions each gene has with other genes within the network. Using the observed interactions we construct a set of differential equations that represent the network dynamics. Using numerical methods and the rough system of equations, we find an approximated system of equations that accurately predicts the dynamics of the normal gene network.
Author: Ioana Marinescu
Publisher:
ISBN: 9780849348310
Category :
Languages : en
Pages :
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Book Description
Author: Guido Sanguinetti
Publisher: Humana
ISBN: 9781493988815
Category : Science
Languages : en
Pages : 0
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Book Description
This volume explores recent techniques for the computational inference of gene regulatory networks (GRNs). The chapters in this book cover topics such as methods to infer GRNs from time-varying data; the extraction of causal information from biological data; GRN inference from multiple heterogeneous data sets; non-parametric and hybrid statistical methods; the joint inference of differential networks; and mechanistic models of gene regulation dynamics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, descriptions of recently developed methods for GRN inference, applications of these methods on real and/ or simulated biological data, and step-by-step tutorials on the usage of associated software tools. Cutting-edge and thorough, Gene Regulatory Networks: Methods and Protocols is an essential tool for evaluating the current research needed to further address the common challenges faced by specialists in this field.
Author: Edwin Wang
Publisher: CRC Press
ISBN: 9781439811863
Category : Computers
Languages : en
Pages : 456
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Book Description
The unprecedented amount of data produced with high-throughput experimentation forces biologists to employ mathematical representation and computation methods to glean meaningful information in systems-level biology. Applying this approach to the underlying molecular mechanisms of tumorigenesis, cancer researchers can uncover a series of new discov
Author: Matan Hofree
Publisher:
ISBN: 9781321532371
Category :
Languages : en
Pages : 146
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Book Description
Genome-wide measurements of genomic state offer unprecedented opportunities for biological discovery, with potential to make dramatic impact on medicine and life. One fundamental challenge is associating complex phenotypes with genetic cause. Here, I will describe efforts to advance solutions to this challenge via analysis of gene networks. Genome-wide association studies are designed link between a phenotype and genomic loci anywhere in the genome; however, applying standard statistics to such data has fallen far short of building accurate predictive models for disease. We use Adaboost, a large-margin classification algorithm, to predict disease status in two cohorts of diabetes and suggest a method for overcoming limitations arising from correlation between genetic variants. We uncover a novel set of 163 disease-associations, missed by `classic' statistics. Classification of cancer remains predominantly organ based and fails to account for considerable heterogeneity of outcomes. Tumor genomes provide a new source of data for uncovering subtypes, but are difficult to compare, as tumors share few mutations in common. We introduce network-based stratification (NBS), a method for integrating somatic genomes with networks encoding biological knowledge. This allows for identification of cancer subtypes by clustering tumors with mutations in similar network regions. We demonstrate NBS in multiple cancer cohorts, identifying subtypes predictive of clinical features and outcomes, and highlighting sub-networks characteristic of each. Current approaches for identifying cancer genes rely on the idea that particular perturbations, occurring in a subset of genes unique to each cancer type, are selected for by conferring a survival advantage to tumor cells. Such genes are expected to be enriched for mutations when examined across a population. Here we show that 30-50% of well-known cancer genes are not significantly elevated in mutation frequency. Despite this lack of enrichment, known cancer genes are enriched for mutations causing changes in amino-acid composition, protein structure properties and conservation. Furthermore, we observe 15-30% of cancer genes have altered mutation rates conditioned on other genes, each individually spanning the range of single-gene mutation frequencies, implicating a large genetic interaction network underlying human cancer. This suggests a substantial number of cancer genes will never be identified by frequency alone.
Author: Matthias Dehmer
Publisher: John Wiley & Sons
ISBN: 3527665455
Category : Medical
Languages : en
Pages : 301
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Book Description
This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.
Author: Chengwei Lei
Publisher:
ISBN: 9781321194784
Category :
Languages : en
Pages : 84
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Book Description
Recent advances in high-throughput technology have dramatically increased the amount of available experimental data in biological research, such as complete genome sequences, transcriptomic data under diverse conditions, and interaction networks among different components in the cell. However, the exponentially increasing data challenges the conventional gene-based paradigm to understand biology. Efficient and effective computational methods are needed to clean, analyze and model the data from a whole systems perspective. To achieve these goals, this research attempts to addresses several key challenging problems in bioinformatics that are associated with constructing functional gene networks and utilizing the networks for better understanding and prediction of cancer development and progression. Specifically, this dissertation has made significant contributions in three relatively independent but highly related sub-areas of bioinformatics. First, an optimization algorithm based on particle swarm intelligence has been developed to efficiently identify transcription factor binding sites (TFBS) motifs that often consist of two short DNA sequence patterns separated by a variable length gap. This work can help decipher the complex gene regulatory networks and understand gene functions. Second, a novel random walk based algorithm has been proposed to remove spurious protein-protein interactions and predict new interactions based solely on the basis of the topological properties of proteins in an existing protein-protein interaction network. Experimental results showed that the method can significantly improve the quality of existing protein-protein interaction networks in yeast and human, which in turn resulted in much better accuracy of protein complex prediction. Finally, new method has been developed to improve cancer prognosis by combining gene expression microarray data and protein-protein interaction networks. Utilizing a random walk algorithm, our method was able to identify novel biomarker genes that can significantly improve the prognosis accuracy of breast cancer metastasis. Importantly, these individual biomarkers are not differentially expressed and therefore would not be detectable by conventionally classification methods that treat individual genes as independent features. Taken together, the results achieved in these diverse sub-areas demonstrated the feasibility of using machine learning approaches to assist biological research at a systems level.
