Computational Methods in Predicting Complex Disease Associated Genes and Environmental Factors
Author | : Yudong Cai |
Publisher | : Frontiers Media SA |
Total Pages | : 212 |
Release | : 2021-06-11 |
Genre | : Science |
ISBN | : 2889668754 |
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Author | : Yudong Cai |
Publisher | : Frontiers Media SA |
Total Pages | : 212 |
Release | : 2021-06-11 |
Genre | : Science |
ISBN | : 2889668754 |
Author | : |
Publisher | : Academic Press |
Total Pages | : 452 |
Release | : 2019-02-06 |
Genre | : Medical |
ISBN | : 0128145145 |
Computational Epigenetics and Diseases, written by leading scientists in this evolving field, provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases. In particular, the major computational tools, databases, and strategies for computational epigenetics analysis, for example, DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA, are summarized, in the context of human diseases. This book discusses bioinformatics methods for epigenetic analysis specifically applied to human conditions such as aging, atherosclerosis, diabetes mellitus, schizophrenia, bipolar disorder, Alzheimer disease, Parkinson disease, liver and autoimmune disorders, and reproductive and respiratory diseases. Additionally, different organ cancers, such as breast, lung, and colon, are discussed. This book is a valuable source for graduate students and researchers in genetics and bioinformatics, and several biomedical field members interested in applying computational epigenetics in their research. - Provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases - Summarizes the major computational tools, databases, and strategies for computational epigenetics analysis, such as DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA - Covers the major milestones and future directions of computational epigenetics in various kinds of human diseases such as aging, atherosclerosis, diabetes, heart disease, neurological disorders, cancers, blood disorders, liver diseases, reproductive diseases, respiratory diseases, autoimmune diseases, human imprinting disorders, and infectious diseases
Author | : George G. Harrigan |
Publisher | : Springer Science & Business Media |
Total Pages | : 342 |
Release | : 2012-12-06 |
Genre | : Science |
ISBN | : 1461503337 |
It is evident that biochemical control is not strictly hierarchical and that intermediary metabolism can contribute to control of regulatory pathways. Metabolic studies are therefore increasingly important in gene function analyses, and an increased interest in metabolites as biomarkers for disease progression or response to therapeutic intervention is also evident in the pharmaceutical industry. This book offers guidelines to currently available technology and bioinformatics and database strategies now being developed. Evidence is presented that metabolic profiling is a valuable addition to genomics and proteomics strategies devoted to drug discovery and development, and that metabolic profiling offers numerous advantages.
Author | : |
Publisher | : Academic Press |
Total Pages | : 211 |
Release | : 2010-11-10 |
Genre | : Science |
ISBN | : 0123808634 |
The field of genetics is rapidly evolving, and new medical breakthroughs are occurring as a result of advances in knowledge gained from genetics reasearch. This thematic volume of Advances in Genetics looks at Computational Methods for Genetics of Complex traits. - Explores the latest topics in neural circuits and behavior research in zebrafish, drosophila, C.elegans, and mouse models - Includes methods for testing with ethical, legal, and social implications - Critically analyzes future prospects
Author | : Miguel Rocha |
Publisher | : Springer Nature |
Total Pages | : 188 |
Release | : 2021-08-27 |
Genre | : Technology & Engineering |
ISBN | : 3030862585 |
This book features novel research papers spanning many different subfields in bioinformatics and computational biology, presenting the latest research on the practical applications to promote fruitful interactions between young researchers in different areas related to the field. Clearly, biology is increasingly becoming a science of information, requiring tools from the computational sciences. To address these challenges, we have seen the emergence of a new generation of interdisciplinary scientists with a strong background in the biological and computational sciences. PACBB'21 expects to contribute to this effort by encouraging a successful collaboration of researchers in different areas related to bioinformatics. The PACBB'21 technical program included 17 papers covering many different subfields in bioinformatics and computational biology. Therefore, this conference, held in Salamanca (Spain), definitely promotes the collaboration of scientists from different research groups and with different backgrounds (computer scientists, mathematicians, biologists) to reach breakthrough solutions for these challenges.
Author | : Xiangxiang Zeng |
Publisher | : MDPI |
Total Pages | : 390 |
Release | : 2019-07-11 |
Genre | : Science |
ISBN | : 3039211951 |
This text will provide the most recent knowledge and advances in the area of molecular computing and bioinformatics. Molecular computing and bioinformatics have a close relationship, paying attention to the same object but working towards different orientations. The articles will range from topics such as DNA computing and membrane computing to specific biomedical applications, including drug R&D and disease analysis.
Author | : Institute of Medicine |
Publisher | : National Academies Press |
Total Pages | : 354 |
Release | : 2012-09-13 |
Genre | : Science |
ISBN | : 0309224187 |
Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.
Author | : Pierre Baldi |
Publisher | : MIT Press (MA) |
Total Pages | : 351 |
Release | : 1998 |
Genre | : Biomolecules |
ISBN | : 9780262024426 |
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory—and this is exactly the situation in molecular biology. As with its predecessor, statistical model fitting, the goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is to automate the process as much as possible. In this book, Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.
Author | : Richard C. Deonier |
Publisher | : Springer Science & Business Media |
Total Pages | : 543 |
Release | : 2005-12-27 |
Genre | : Computers |
ISBN | : 0387288074 |
This book presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book features a free download of the R software statistics package and the text provides great crossover material that is interesting and accessible to students in biology, mathematics, statistics and computer science. More than 100 illustrations and diagrams reinforce concepts and present key results from the primary literature. Exercises are given at the end of chapters.
Author | : Cedric Gondro |
Publisher | : Humana Press |
Total Pages | : 0 |
Release | : 2013-06-12 |
Genre | : Science |
ISBN | : 9781627034463 |
With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.