Cancer Gene Networks PDF Download
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Author | : Usha Kasid |
Publisher | : Methods in Molecular Biology |
Total Pages | : 262 |
Release | : 2018-11-17 |
Genre | : Medical |
ISBN | : 9781493982301 |
Download Cancer Gene Networks Book in PDF, ePub and Kindle
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 | : Shruti Mishra |
Publisher | : Academic Press |
Total Pages | : 133 |
Release | : 2018-05-09 |
Genre | : Science |
ISBN | : 0128163577 |
Download Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome Book in PDF, ePub and Kindle
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 | : Ioana Marinescu |
Publisher | : |
Total Pages | : |
Release | : 1995-01-01 |
Genre | : |
ISBN | : 9780849348310 |
Download Gene Networks in Cancer Genesis and Reversion Book in PDF, ePub and Kindle
Author | : Paul Scott |
Publisher | : |
Total Pages | : 54 |
Release | : 2017 |
Genre | : Electronic dissertations |
ISBN | : |
Download Dynamics of Gene Networks in Cancer Research Book in PDF, ePub and Kindle
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 | : Alberto Fuente |
Publisher | : Springer Science & Business Media |
Total Pages | : 135 |
Release | : 2014-01-03 |
Genre | : Science |
ISBN | : 3642451616 |
Download Gene Network Inference Book in PDF, ePub and Kindle
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 | : Guido Sanguinetti |
Publisher | : Humana |
Total Pages | : 0 |
Release | : 2018-12-14 |
Genre | : Science |
ISBN | : 9781493988815 |
Download Gene Regulatory Networks Book in PDF, ePub and Kindle
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 |
Total Pages | : 456 |
Release | : 2010-05-04 |
Genre | : Computers |
ISBN | : 9781439811863 |
Download Cancer Systems Biology Book in PDF, ePub and Kindle
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 | : |
Total Pages | : 146 |
Release | : 2014 |
Genre | : |
ISBN | : 9781321532371 |
Download Analysis of Genomic Variants Via Gene Networks Book in PDF, ePub and Kindle
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 |
Total Pages | : 301 |
Release | : 2012-11-28 |
Genre | : Medical |
ISBN | : 3527665455 |
Download Statistical Diagnostics for Cancer Book in PDF, ePub and Kindle
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 | : Deodutta Roy |
Publisher | : Springer Science & Business Media |
Total Pages | : 521 |
Release | : 2010-09-11 |
Genre | : Medical |
ISBN | : 1441967524 |
Download Environmental Factors, Genes, and the Development of Human Cancers Book in PDF, ePub and Kindle
Cancer is a complex disease. Only 5-10% of human cancers are hereditary in nature. Many of us think of environmental agents when we think of carcinogens. The environment includes all that surrounds us, and environmental influences include not only chemical, physical and biological toxicants, but also diet and lifestyle. In this broadest sense, the environment contributes substantially in the development of human cancer. This book will describe how environment contributes to malignant transformation leading to profound changes in the genetic and signaling networks that control the functioning of the cell. It will critically discuss the understanding of the effects of environment on the development, progression and metastasis of cancer with current knowledge of the signaling networks that support functioning of transformed human cells. Genes and environmental factors that influence the origins of cancer are not necessarily the same as those that contribute to its progression and metastasis. Susceptibility gene variants for each specific cancer are being identified with emerging evidence of gene–environment interaction. Gene-environment interactions will be discussed through each specific cancer-based approach to address the question of how genetic variations can influence susceptibility to the individual type of cancer. It will also highlight and summarize epigenetic changes that increase the risk for susceptibility to a particular type of cancer, particularly in the presence of specific environmental factors. Thus, this book will contain chapters from the world’s experts focused on the current evidences that support the role of environment in the cancer etiology and in the growth of malignant lesions, and discuss who may be susceptible to environmental influences.