Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine PDF Download
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Author: Ehsan Nazemalhosseini-Mojarad
Publisher: Frontiers Media SA
ISBN: 2832530389
Category : Science
Languages : en
Pages : 433
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Book Description
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.
Author: Ehsan Nazemalhosseini-Mojarad
Publisher: Frontiers Media SA
ISBN: 2832530389
Category : Science
Languages : en
Pages : 433
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Book Description
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.
Author: Leonardo Rundo
Publisher: Mdpi AG
ISBN: 9783036525549
Category : Science
Languages : en
Pages : 262
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Book Description
Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians' unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations-such as segmentation, co-registration, classification, and dimensionality reduction-and multi-omics data integration.
Author: Alessandro Laganà
Publisher: Springer Nature
ISBN: 303091836X
Category : Medical
Languages : en
Pages : 341
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Book Description
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.
Author: Tao Zeng
Publisher: Frontiers Media SA
ISBN: 2889635546
Category :
Languages : en
Pages : 393
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Book Description
Author: Abedalrhman Alkhateeb
Publisher: Springer Nature
ISBN: 303136502X
Category : Science
Languages : en
Pages : 171
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Book Description
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.
Author: Hao Ding
Publisher:
ISBN:
Category :
Languages : en
Pages : 135
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Book Description
Understanding and characterizing cancer heterogeneity not only generates new mechanistic insights but can also lead to personalized treatments for patients. With advances in data generation technologies, ever-increasing amounts and types of multi-omics open great opportunities for researchers to gain extremely valuable information for cancer research and clinical biomarker discovery. However, the vast and complex nature of multi-omics data pose significant challenges regarding the extraction of useful information and the effective integration of multiple types of data. This dissertation tackles the problem of multi-omics data analysis through both visual analytics and computational angles. First, we present GRAPh based Histology Image Explorer (GRAPHIE), a visual analytics tool designed to explore, annotate, and discover potential relationships in phenomics datasets (histology images). By taking a data-driven approach, we developed an unbiased way to visualize the entire dataset with node-link graphs. The intuitive visualization and rich set of interactive functions allow users to effectively explore the dataset. While (GRAPHIE) focusing on analysising the histological information, we present the second visual analytics tool, integrative Genomic Patient Stratification explorer (iGPSe) which leverages multiple types of molecular features to further characterize patients and tumors. iGPSe is designed to assist researchers in effectively performing integrative multi-omics analysis through interactive visualization components. The tool integrates unsupervised clustering with graph and parallel sets visualization and allows a direct comparison of clinical outcomes via survival analysis. For both tools, we comprehensively analyzed the design requirements and carried out users' case studies to demonstrated the usefulness. Lastly, we developed a computational method that can jointly cluster cancer patient samples based on multi-omics data. The proposed method creates a patient-to-patient similarity graph for each data type as an intermediate representation of each omics data type and merges the graphs through subspace analysis on a Grassmann manifold. We applied our approach to a breast cancer dataset and showed that by integrating gene expression, microRNA, and DNA methylation data, the proposed method would produce potentially clinically useful subtypes of breast cancer. The proposed visual analytics tools and computational method can be extended to more generalized applications in which exploration and integration of multi-omics data are needed. This dissertation also provides high-level design considerations for visual analytics tools to conceptual methodologies in integrative analysis to future researchers and practitioners for devising effective multi-omics data analysis.
Author: Inna Kuperstein
Publisher: CRC Press
ISBN: 1000682927
Category : Computers
Languages : en
Pages : 167
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Book Description
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’
Author: Francisco A. Gómez Vela
Publisher: MDPI
ISBN: 3039437712
Category : Medical
Languages : en
Pages : 222
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Book Description
In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Author: Paolo Tieri
Publisher: Frontiers Media SA
ISBN: 2889196488
Category : Science (General)
Languages : en
Pages : 137
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Book Description
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.
Author: Nataša Pržulj
Publisher: Cambridge University Press
ISBN: 1108432239
Category : Language Arts & Disciplines
Languages : en
Pages : 647
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Book Description
Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples.
