Download or read book Cancer Gene Networks written by Usha Kasid and published by Methods in Molecular Biology. This book was released on 2018-11-17 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Dynamics of Gene Networks in Cancer Research written by Paul Scott and published by . This book was released on 2017 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome written by Shruti Mishra and published by Academic Press. This book was released on 2018-05-09 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Gene Networks in Cancer Genesis and Reversion written by Ioana Marinescu and published by . This book was released on 1995-01-01 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Gene Regulatory Networks written by Guido Sanguinetti and published by Humana. This book was released on 2018-12-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Cancer Systems Biology written by Edwin Wang and published by CRC Press. This book was released on 2010-05-04 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Analysis of Genomic Variants Via Gene Networks written by Matan Hofree and published by . This book was released on 2014 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Statistical Diagnostics for Cancer written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2012-11-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Biological Network Reconstruction Denoising and Applications in Cancer Classification written by Chengwei Lei and published by . This book was released on 2014 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Environmental Factors Genes and the Development of Human Cancers written by Deodutta Roy and published by Springer Science & Business Media. This book was released on 2010-09-11 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Reverse engineering of Genetic Regulatory Pathways in Human Cancer written by Yikan Wang and published by . This book was released on 2013 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microarray-based gene expression profiling, and more recently RNA sequencing, have been widely used in cancer research and have provided valuable insights into the molecular mechanisms underlying cancer. The research presented in this thesis uses data-driven computational models to interpret tumour gene expression information in the context of regulatory network inference, identification of modulators of regulation and tumour classification. Firstly, an ordinary differential equation (ODE) regression-based reverse-engineering algorithm, MIKANA, is extended to reconstruct gene regulatory interactions from both steady-state and time-series measurements simultaneously. Inferring gene networks from a combination of steady-state and time-series data is found to be especially advantageous when using noisy time-series measurements collected with either lower sampling rates or limited number of experimental replicates. When applied to human datasets this approach is found to reveal biology that cannot be revealed by steady-state or dynamic models individually. By incorporating combinatorial interactions, in which the action of one regulating gene on its downstream target is modified by another 'modulator' gene, the method is further extended to identify both molecular and clinical factors that may control the activities of transcription factors (TFs). This new method adopts the concept of three-way interactions to identify candidate modulators of TF-target genes interaction from gene expression data without making any prior biological assumptions. The method is applied to cancer-related transcription factors, and the inferred modulators are shown to be statistically and biologically significant for the corresponding transcriptional modules. Finally, a previously published biclustering approach, cMonkey, is adopted to identify molecular-based tumour subclasses (MetaChips) by searching for similarity in the expression of subsets of genes across subsets of tumours. Application of the method to breast cancer data shows that tumours in the same MetaChip present similar clinico-pathological features. Tumour samples in different MetaChips are molecularly and clinico-pathologically distinct. A conditional inference tree-based survival prediction model is built from the combination of clinical information and the membership of MetaChips. It is shown that prediction of patient's early relapse is improved by incorporating these molecular-based tumour subclasses, compared with the prediction from the model with conventional clinical variables only.

Download or read book A Systems Biology Approach to Characterizing Gene Fusion Pathways in Cancer written by Tressa R. Hood and published by . This book was released on 2017 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene fusions have long been known to drive cancer. Initial discovery of gene fusions was opportunistic, and functional assessment was done individually and experimentally. There is no comprehensive systems biology approach to understanding the impact of gene fusions on the signaling networks within tumor cells. An integrative computational approach was taken to achieve a better understanding of gene fusions and their complex influence on pathways and interaction networks in the context of lung cancer. Using well-studied fusions and publicly available gene expression data, the effect of fusion events on the expression pattern of gene networks revealed unique differences in tumors with gene fusions, tumors without gene fusions, and normal samples. This approach identifies gene expression signatures associated with specific fusions, and provides a model for integrating experimental and pathway data to better understand the biology of a fusion genes and their roles in oncogenesis.

Download or read book Computational Cancer Biology written by Mathukumalli Vidyasagar and published by Springer Science & Business Media. This book was released on 2012-11-28 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief introduces people with a basic background in probability theory to various problems in cancer biology that are amenable to analysis using methods of probability theory and statistics. The title mentions “cancer biology” and the specific illustrative applications reference cancer data but the methods themselves are more broadly applicable to all aspects of computational biology. Aside from providing a self-contained introduction to basic biology and to cancer, the brief describes four specific problems in cancer biology that are amenable to the application of probability-based methods. The application of these methods is illustrated by applying each of them to actual data from the biology literature. After reading the brief, engineers and mathematicians should be able to collaborate fruitfully with their biologist colleagues on a wide variety of problems.

Download or read book Genome Chaos written by Henry H. Heng and published by Academic Press. This book was released on 2019-05-25 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome Chaos: Rethinking Genetics, Evolution, and Molecular Medicine transports readers from Mendelian Genetics to 4D-genomics, building a case for genes and genomes as distinct biological entities, and positing that the genome, rather than individual genes, defines system inheritance and represents a clear unit of selection for macro-evolution. In authoring this thought-provoking text, Dr. Heng invigorates fresh discussions in genome theory and helps readers reevaluate their current understanding of human genetics, evolution, and new pathways for advancing molecular and precision medicine. Bridges basic research and clinical application and provides a foundation for re-examining the results of large-scale omics studies and advancing molecular medicine Gathers the most pressing questions in genomic and cytogenomic research Offers alternative explanations to timely puzzles in the field Contains eight evidence-based chapters that discuss 4d-genomics, genes and genomes as distinct biological entities, genome chaos and macro-cellular evolution, evolutionary cytogenetics and cancer, chromosomal coding and fuzzy inheritance, and more

Download or read book Systems Biology Applications In Cancer related Research written by Hsueh-fen Juan and published by World Scientific. This book was released on 2012-02-29 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents an overview of recent developments in systems biology and their applications in cancer-related research. The ongoing advances in our understanding of genomics and proteomics, coupled with the development of new and more robust tools, have led to an emphasis on analyzing biological systems at multiple levels. Thus, there is a need to integrate different types of data into a comprehensive “systems” view.Written by active researchers in the emerging areas, this book gives senior undergraduate students, graduate students and new researchers an idea of where the frontiers of systems biology are and an opportunity to learn high-throughput techniques in use. One of the particular emphases of the book is to elucidate the molecular mechanisms in cancer. The discovery of biomarkers and anti-cancer drugs using systems biology approach is also extensively discussed.

Download or read book Mammography and Beyond written by National Research Council and published by National Academies Press. This book was released on 2001-06-04 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: X-ray mammography screening is the current mainstay for early breast cancer detection. It has been proven to detect breast cancer at an earlier stage and to reduce the number of women dying from the disease. However, it has a number of limitations. These current limitations in early breast cancer detection technology are driving a surge of new technological developments, from modifications of x-ray mammography such as computer programs that can indicate suspicious areas, to newer methods of detection such as magnetic resonance imaging (MRI) or biochemical tests on breast fluids. To explore the merits and drawbacks of these new breast cancer detection techniques, the Institute of Medicine of the National Academy of Sciences convened a committee of experts. During its year of operation, the committee examined the peer-reviewed literature, consulted with other experts in the field, and held two public workshops. In addition to identifying promising new technologies for early detection, the committee explored potential barriers that might prevent the development of new detection methods and their common usage. Such barriers could include lack of funding from agencies that support research and lack of investment in the commercial sector; complicated, inconsistent, or unpredictable federal regulations; inadequate insurance reimbursement; and limited access to or unacceptability of breast cancer detection technology for women and their doctors. Based on the findings of their study, the committee prepared a report entitled Mammography and Beyond: Developing Technology for Early Detection of Breast Cancer, which was published in the spring of 2001. This is a non-technical summary of that report.