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Computational Methods For Multi Omics Data Analysis In Cancer Precision Medicine
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Book Synopsis Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine by : Ehsan Nazemalhosseini-Mojarad
Download or read book Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine written by Ehsan Nazemalhosseini-Mojarad and published by Frontiers Media SA. This book was released on 2023-08-02 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels: i) DNA with genome-wide association studies (GWAS), ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs) iii) mRNA. The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.
Book Synopsis Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine by : Tao Zeng
Download or read book Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine written by Tao Zeng and published by Frontiers Media SA. This book was released on 2020-03-30 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book DNA Methylation written by J. Jost and published by Birkhäuser. This book was released on 2013-11-11 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: The occurrence of 5-methylcytosine in DNA was first described in 1948 by Hotchkiss (see first chapter). Recognition of its possible physiologi cal role in eucaryotes was first suggested in 1964 by Srinivasan and Borek (see first chapter). Since then work in a great many laboratories has established both the ubiquity of 5-methylcytosine and the catholicity of its possible regulatory function. The explosive increase in the number of publications dealing with DNA methylation attests to its importance and makes it impossible to write a comprehensive coverage of the literature within the scope of a general review. Since the publication of the 3 most recent books dealing with the subject (DNA methylation by Razin A. , Cedar H. and Riggs A. D. , 1984 Springer Verlag; Molecular Biology of DNA methylation by Adams R. L. P. and Burdon R. H. , 1985 Springer Verlag; Nucleic Acids Methylation, UCLA Symposium suppl. 128, 1989) considerable progress both in the techniques and results has been made in the field of DNA methylation. Thus we asked several authors to write chapters dealing with aspects of DNA methyla tion in which they are experts. This book should be most useful for students, teachers as well as researchers in the field of differentiation and gene regulation. We are most grateful to all our colleagues who were willing to spend much time and effort on the publication of this book. We also want to express our gratitude to Yan Chim Jost for her help in preparing this book.
Book Synopsis Evolution of Translational Omics by : Institute of Medicine
Download or read book Evolution of Translational Omics written by Institute of Medicine and published by National Academies Press. This book was released on 2012-09-13 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Computational Genomics with R by : Altuna Akalin
Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
Book Synopsis Analyzing Network Data in Biology and Medicine by : Nataša Pržulj
Download or read book Analyzing Network Data in Biology and Medicine written by Nataša Pržulj and published by Cambridge University Press. This book was released on 2019-03-28 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.
Book Synopsis Computational Methods for Precision Oncology by : Alessandro Laganà
Download or read book Computational Methods for Precision Oncology written by Alessandro Laganà and published by Springer Nature. This book was released on 2022-03-01 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precision medicine holds great promise for the treatment of cancer and represents a unique opportunity for accelerated development and application of novel and repurposed therapeutic approaches. Current studies and clinical trials demonstrate the benefits of genomic profiling for patients whose cancer is driven by specific, targetable alterations. However, precision oncologists continue to be challenged by the widespread heterogeneity of cancer genomes and drug responses in designing personalized treatments. Chapters provide a comprehensive overview of the computational approaches, methods, and tools that enable precision oncology, as well as related biological concepts. Covered topics include genome sequencing, the architecture of a precision oncology workflow, and introduces cutting-edge research topics in the field of precision oncology. This book is intended for computational biologists, bioinformaticians, biostatisticians and computational pathologists working in precision oncology and related fields, including cancer genomics, systems biology, and immuno-oncology.
Book Synopsis Multi-Omics Analysis of the Human Microbiome by : Indra Mani
Download or read book Multi-Omics Analysis of the Human Microbiome written by Indra Mani and published by Springer Nature. This book was released on with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Systems Biology Approaches in Cancer Research by : Inna Kuperstein
Download or read book Computational Systems Biology Approaches in Cancer Research written by Inna Kuperstein and published by CRC Press. This book was released on 2019-09-09 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Computational Systems BiologyApproaches in Cancer Research: "Complex concepts are written clearly and with informative illustrations and useful links. The book is enjoyable to read yet provides sufficient depth to serve as a valuable resource for both students and faculty." — Trey Ideker, Professor of Medicine, UC Xan Diego, School of Medicine "This volume is attractive because it addresses important and timely topics for research and teaching on computational methods in cancer research. It covers a broad variety of approaches, exposes recent innovations in computational methods, and provides acces to source code and to dedicated interactive web sites." — Yves Moreau, Department of Electrical Engineering, SysBioSys Centre for Computational Systems Biology, University of Leuven With the availability of massive amounts of data in biology, the need for advanced computational tools and techniques is becoming increasingly important and key in understanding biology in disease and healthy states. This book focuses on computational systems biology approaches, with a particular lens on tackling one of the most challenging diseases - cancer. The book provides an important reference and teaching material in the field of computational biology in general and cancer systems biology in particular. The book presents a list of modern approaches in systems biology with application to cancer research and beyond. It is structured in a didactic form such that the idea of each approach can easily be grasped from the short text and self-explanatory figures. The coverage of topics is diverse: from pathway resources, through methods for data analysis and single data analysis to drug response predictors, classifiers and image analysis using machine learning and artificial intelligence approaches. Features Up to date using a wide range of approaches Applicationexample in each chapter Online resources with useful applications’
Book Synopsis Learning to Classify Text Using Support Vector Machines by : Thorsten Joachims
Download or read book Learning to Classify Text Using Support Vector Machines written by Thorsten Joachims and published by Springer Science & Business Media. This book was released on 2002-04-30 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
Book Synopsis Big Data in Omics and Imaging by : Momiao Xiong
Download or read book Big Data in Omics and Imaging written by Momiao Xiong and published by CRC Press. This book was released on 2017-12-01 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.
Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Walter Daelemans
Download or read book Machine Learning and Knowledge Discovery in Databases written by Walter Daelemans and published by Springer Science & Business Media. This book was released on 2008-09-04 with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. 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. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
Book Synopsis Artificial Intelligence in Design ’96 by : John S. Gero
Download or read book Artificial Intelligence in Design ’96 written by John S. Gero and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: Change is one of the most significant parameters in our society. Designers are amongst the primary change agents for any society. As a consequence design is an important research topic in engineering and architecture and related disciplines, since design is not only a means of change but is also one of the keystones to economic competitiveness and the fundamental precursor to manufacturing. The development of computational models founded on the artificial intelligence paradigm has provided an impetus for much of current design research -both computational and cognitive. These forms of design research have only been carried out in the last decade or so and in the temporal sense they are still immature. Notwithstanding this immaturity, noticeable advances have been made both in extending our understanding of design and in developing tools based on that understanding. Whilst many researchers in the field of artificial intelligence in design utilise ideas about how humans design as one source of concepts there is normally no attempt to model human designers. Rather the results of the research presented in this volume demonstrate approaches to increasing our understanding of design as a process.
Book Synopsis Machine Learning Methods for Multi-Omics Data Integration by : Abedalrhman Alkhateeb
Download or read book Machine Learning Methods for Multi-Omics Data Integration written by Abedalrhman Alkhateeb and published by Springer Nature. This book was released on 2023-12-15 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.
Book Synopsis Advances in methods and tools for multi-omics data analysis by : Ornella Cominetti
Download or read book Advances in methods and tools for multi-omics data analysis written by Ornella Cominetti and published by Frontiers Media SA. This book was released on 2023-05-12 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multi-omic Data Integration by : Paolo Tieri
Download or read book Multi-omic Data Integration written by Paolo Tieri and published by Frontiers Media SA. This book was released on 2015-09-17 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stable, predictive biomarkers and interpretable disease signatures are seen as a significant step towards personalized medicine. In this perspective, integration of multi-omic data coming from genomics, transcriptomics, glycomics, proteomics, metabolomics is a powerful strategy to reconstruct and analyse complex multi-dimensional interactions, enabling deeper mechanistic and medical insight. At the same time, there is a rising concern that much of such different omic data –although often publicly and freely available- lie in databases and repositories underutilised or not used at all. Issues coming from lack of standardisation and shared biological identities are also well-known. From these considerations, a novel, pressing request arises from the life sciences to design methodologies and approaches that allow for these data to be interpreted as a whole, i.e. as intertwined molecular signatures containing genes, proteins, mRNAs and miRNAs, able to capture inter-layers connections and complexity. Papers discuss data integration approaches and methods of several types and extents, their application in understanding the pathogenesis of specific diseases or in identifying candidate biomarkers to exploit the full benefit of multi-omic datasets and their intrinsic information content. Topics of interest include, but are not limited to: • Methods for the integration of layered data, including, but not limited to, genomics, transcriptomics, glycomics, proteomics, metabolomics; • Application of multi-omic data integration approaches for diagnostic biomarker discovery in any field of the life sciences; • Innovative approaches for the analysis and the visualization of multi-omic datasets; • Methods and applications for systematic measurements from single/undivided samples (comprising genomic, transcriptomic, proteomic, metabolomic measurements, among others); • Multi-scale approaches for integrated dynamic modelling and simulation; • Implementation of applications, computational resources and repositories devoted to data integration including, but not limited to, data warehousing, database federation, semantic integration, service-oriented and/or wiki integration; • Issues related to the definition and implementation of standards, shared identities and semantics, with particular focus on the integration problem. Research papers, reviews and short communications on all topics related to the above issues were welcomed.
Book Synopsis Omics for Personalized Medicine by : Debmalya Barh
Download or read book Omics for Personalized Medicine written by Debmalya Barh and published by Springer Science & Business Media. This book was released on 2013-10-14 with total page 825 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Omics for Personalized Medicine” will give to its prospective readers the insight of both the current developments and the future potential of personalized medicine. The book brings into light how the pharmacogenomics and omics technologies are bringing a revolution in transforming the medicine and the health care sector for the better. Students of biomedical research and medicine along with medical professionals will benefit tremendously from the book by gaining from the diverse fields of knowledge of new age personalized medicine presented in the highly detailed chapters of the book. The book chapters are divided into two sections for convenient reading with the first section covering the general aspects of pharmaocogenomic technology that includes latest research and development in omics technologies. The first section also highlights the role of omics in modern clinical trials and even discusses the ethical consideration in pharmocogenomics. The second section is focusing on the development of personalized medicine in several areas of human health. The topics covered range from metabolic and neurological disorders to non-communicable as well as infectious diseases, and even explores the role of pharmacogenomics in cell therapy and transplantation technology. Thirty-four chapters of the book cover several aspects of pharmacogenomics and personalized medicine and have taken into consideration the varied interest of the readers from different fields of biomedical research and medicine. Advent of pharmacogenomics is the future of modern medicine, which has resulted from culmination of decades of research and now is showing the way forward. The book is an honest endeavour of researchers from all over the world to disseminate the latest knowledge and knowhow in personalized medicine to the community health researchers in particular and the educated public in general.
