Download or read book Integration of Multisource Heterogenous Omics Information in Cancer written by Victor Jin and published by Frontiers Media SA. This book was released on 2020-01-30 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multisource heterogenous omics data can provide unprecedented perspectives and insights into cancer studies, but also pose great analytical problems for researchers due to the vast amount of data produced. This Research Topic aims to provide a forum for sharing ideas, tools and results among researchers from various computational cancer biology fields such as genetic/epigenetic and genome-wide studies.

Download or read book Multi omic Data Integration in Oncology written by Chiara Romualdi and published by Frontiers Media SA. This book was released on 2020-12-03 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

Download or read book Systems Analytics and Integration of Big Omics Data written by Gary Hardiman and published by MDPI. This book was released on 2020-04-15 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: A “genotype" is essentially an organism's full hereditary information which is obtained from its parents. A "phenotype" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.

Download or read book Integration of Omics Approaches and Systems Biology for Clinical Applications written by Antonia Vlahou and published by John Wiley & Sons. This book was released on 2018-01-24 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces readers to the state of the art of omics platforms and all aspects of omics approaches for clinical applications This book presents different high throughput omics platforms used to analyze tissue, plasma, and urine. The reader is introduced to state of the art analytical approaches (sample preparation and instrumentation) related to proteomics, peptidomics, transcriptomics, and metabolomics. In addition, the book highlights innovative approaches using bioinformatics, urine miRNAs, and MALDI tissue imaging in the context of clinical applications. Particular emphasis is put on integration of data generated from these different platforms in order to uncover the molecular landscape of diseases. The relevance of each approach to the clinical setting is explained and future applications for patient monitoring or treatment are discussed. Integration of omics Approaches and Systems Biology for Clinical Applications presents an overview of state of the art omics techniques. These methods are employed in order to obtain the comprehensive molecular profile of biological specimens. In addition, computational tools are used for organizing and integrating these multi-source data towards developing molecular models that reflect the pathophysiology of diseases. Investigation of chronic kidney disease (CKD) and bladder cancer are used as test cases. These represent multi-factorial, highly heterogeneous diseases, and are among the most significant health issues in developed countries with a rapidly aging population. The book presents novel insights on CKD and bladder cancer obtained by omics data integration as an example of the application of systems biology in the clinical setting. Describes a range of state of the art omics analytical platforms Covers all aspects of the systems biology approach—from sample preparation to data integration and bioinformatics analysis Contains specific examples of omics methods applied in the investigation of human diseases (Chronic Kidney Disease, Bladder Cancer) Integration of omics Approaches and Systems Biology for Clinical Applications will appeal to a wide spectrum of scientists including biologists, biotechnologists, biochemists, biophysicists, and bioinformaticians working on the different molecular platforms. It is also an excellent text for students interested in these fields.

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.

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.

Download or read book Integration of Multi omics Data with Graph Convolutional Networks to Identify Cancer associated Genes written by Roman Schulte-Sasse and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Integration of Online Omics data Resources for Cancer Research written by Tonmoy Das and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The manifestations of cancerous phenotypes necessitate alterations at different levelsof information-flow from genome to proteome. The molecular alterations at differentinformation processing levels serve as the basis for the cancer phenotype to emerge. Tounderstand the underlying mechanisms that drive the acquisition of cancer hallmarks it isrequired to interrogate cancer cells using multiple levels of information flow representedby different omics - such as genomics, epigenomics, transcriptomics, and proteomics.The advantage of multi-omics data integration comes with a trade-off in the form of anadded layer of complexity originating from inherently diverse types of omics-datasetsthat may pose a challenge to integrate the omics-data in a biologically meaningfulmanner. The plethora of cancer-specific online omics-data resources, if able to beintegrated efficiently and systematically, may facilitate the generation of new biologicalinsights for cancer research. In this review, we provide a comprehensive overviewof the online single- and multi-omics resources that are dedicated to cancer. Wecatalog various online omics-data resources such as The Cancer Genome Atlas (TCGA)along with various TCGA-associated data portals and tools for multi-omics analysisand visualization, the International Cancer Genome Consortium (ICGC), Catalogueof Somatic Mutations in Cancer (COSMIC), The Pathology Atlas, Gene ExpressionOmnibus (GEO), and PRoteomics IDEntifications (PRIDE). By comparing the strengthsand limitations of the respective online resources, we aim to highlight the currentbiological and technological challenges and possible strategies to overcome thesechallenges. We outline the available schemes for the integration of the multi-omicsdimensions for stratifying cancer patients and biomarker prediction based on theintegrated molecular-signatures of cancer. Finally, we propose the multi-omics drivensystems-biology approaches to realize the potential of precision onco-medicine as the uture of cancer research. We believe this systematic review will encourage scientistsand clinicians worldwide to utilize the online resources to explore and integrate theavailable omics datasets that may provide a window of opportunity to generate newbiological insights and contribute to the advancement of the field of cancer research

Download or read book Methodologies of Multi Omics Data Integration and Data Mining written by Kang Ning and published by Springer Nature. This book was released on 2023-01-15 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the “What”, “Why” and “How” of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches.

Download or read book Integrative Multi omics Analysis to Understand Cancer and Anticancer Therapy written by Michelle Ting Dow and published by . This book was released on 2020 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precision cancer medicine promises better treatments to a disease as complex and heterogenous as cancer. Many anti-cancer therapies are beneficial to only a subset of patients due to the variability in patient genetic and tumor heterogeneity. Thus, we need better frameworks for understanding underlying genomic and transcriptomic patterns influencing differential patient outcomes, yet our understanding of how genetic alterations connect to treatment in in vivo and in vitro models remains understudied. To address this gap, I utilized human patient data from The Cancer Genome Atlas (TCGA), hepatocellular carcinoma (HCC) models, prostate cancer (PCa) models, and chronic myelogenous leukemia (CML) cell lines. Through the integration of multi-omic data, I identified parallel features of human and model organism data that could reveal disease specific characteristics. Additionally, I characterized the landscape of acquired resistance for a panel of chemotherapeutic treatments and revealed potential alleles and genes that mediate the process. The analyses I conducted expose the role of genetic information and suggest future applications for development of precision 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.

Download or read book Genomics and Bioinformatics written by Tore Samuelsson and published by Cambridge University Press. This book was released on 2012-06-07 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the arrival of genomics and genome sequencing projects, biology has been transformed into an incredibly data-rich science. The vast amount of information generated has made computational analysis critical and has increased demand for skilled bioinformaticians. Designed for biologists without previous programming experience, this textbook provides a hands-on introduction to Unix, Perl and other tools used in sequence bioinformatics. Relevant biological topics are used throughout the book and are combined with practical bioinformatics examples, leading students through the process from biological problem to computational solution. All of the Perl scripts, sequence and database files used in the book are available for download at the accompanying website, allowing the reader to easily follow each example using their own computer. Programming examples are kept at an introductory level, avoiding complex mathematics that students often find daunting. The book demonstrates that even simple programs can provide powerful solutions to many complex bioinformatics problems.

Download or read book Leveraging Multi omics Integration to Characterize Human Cancer written by Akshay Sanghi and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Omic technologies have been applied to cancer since they were invented and have yielded extremely powerful results. Common tumor types have been profiled extensively for their cancer drivers and genetic landscapes, identifying the extent of to which mutations alter the gene expression in cancer. However, other molecular features have not been comprehensively studied. As omic technologies become more widely available and more accurate, the unprofiled molecular landscape of cancer is ripe for investigation. This dissertation focuses on scientific investigations that utilize state-of-the-art omic tools to characterize the molecular phenotypes in human cancers. In addition, this dissertation addresses the topic of diversity, equity, and inclusion (DEI) and shares results from a program that I founded for Stanford Biosciences and the Medical Scientist Training Program (MSTP). The first chapter provides an overview of gene regulation studies based on multi-omics methodology, current molecular knowledge of thyroid cancer, and the state of DEI in biomedical graduate training programs. Chapter 2 clarifies mechanisms in which chromatin regulation affects protein-transcript correlation in progression and development of thyroid and breast cancer. It points to an interesting mechanism by which transcription factors mediate coordinated transcription and translation. Chapter 3 outlines the preliminary findings from a multi-omic analysis of genotype, chromatin regulatory landscape, transcripts, and proteins in metastatic thyroid cancer. This analysis classifies tumors and metastases according to regulons that have direct and indirect effects on the comprehensive profile of biomolecules. Chapter 4 describes an approach to a diversity recruitment program that has potential to scale across biomedical graduate programs. Results from the program show there are many underrepresented minorities that have the potential to thrive in biomedical training if supported. Lastly, Chapter 5 discusses the potential impact of multi-omics integration on the cancer field and other disease types. Taken together, these chapters represent key insights, improvements, and applications of multi-omics integration on human disease and opportunities for our graduate programs to be more engaged with DEI.

Download or read book Kernel Methods in Computational Biology written by Bernhard Schölkopf and published by MIT Press. This book was released on 2004 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed overview of current research in kernel methods and their application to computational biology.

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:

Download or read book Integrated Analysis of Multi omics Data Using Sparse Canonical Correlation Analysis written by Alissa Marie Castleberry and published by . This book was released on 2019 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: Results Data analysis using head and neck squamous cell carcinoma samples from the Cancer Genome Atlas revealed that many of the miRNA-mRNA interactions of interest were on cancer or cancer-related pathways, such as the p53-signaling or melanoma pathways. Data analysis using microbe and metabolite data taken from colorectal cancer patients revealed that many of the metabolites produced by microbes of interest, such as tyrosine and lysylproline, were found on cancer or cancer-related pathways, like central carbon metabolism in cancer. Conclusion Using SCCA to inform a gene or metabolite set enrichment analysis is more informative than using simply a pairwise correlation analysis, as it better takes into account the complexity present in real biology. The performance of the proposed method shows promise for identifying biological pathways enriched for genes regulated by miRNA expression or metabolites produced by a certain set of microbes, and could be used to model other multi-omics datasets assuming a relationship between the two can be established.

Download or read book Artificial Intelligence written by and published by Elsevier. This book was released on 2023-09-11 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence, Volume 49 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics. Chapters in this new release include AI Teacher-Student based Adaptive Structural Deep Learning Model and Its Estimating Uncertainty of Image Data, Machine-derived Intelligence: Computations Beyond the Null Hypothesis, Object oriented basis of artificial intelligence methodologies I in Judicial Systems in India, Artificial Intelligence in Systems Biology, Machine-Learning in Geometry and Physics, Innovation and Machine Learning: Crowdsourcing Open-Source Natural Language Processing (NLP) Algorithms to Advance Public Health Surveillance, and more. Other chapters cover Learning and identity testing of Markov chains, Data privacy for machine learning and statistics, and The interface between AI and Mathematics. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Handbook of Statistics series - Includes the latest information on Artificial Intelligence