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| cite seq data analysis: Computational Methods for Single-Cell Data Analysis Guo-Cheng Yuan, 2019-02-14 This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. 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, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis. |
| cite seq data analysis: Next-Generation Sequencing Data Analysis Xinkun Wang, 2016-04-06 A Practical Guide to the Highly Dynamic Area of Massively Parallel SequencingThe development of genome and transcriptome sequencing technologies has led to a paradigm shift in life science research and disease diagnosis and prevention. Scientists are now able to see how human diseases and phenotypic changes are connected to DNA mutation, polymorphi |
| cite seq data analysis: Handbook of Statistical Bioinformatics Henry Horng-Shing Lu, Bernhard Schölkopf, Martin T. Wells, Hongyu Zhao, 2022-12-08 Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology. |
| cite seq data analysis: Compositional Data Analysis Vera Pawlowsky-Glahn, Antonella Buccianti, 2011-09-19 It is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more difficult to realize that so many statisticians and users of statistics are unaware of the particular problems affecting compositional data, as well as their solutions. The issue of ``spurious correlation'', as the situation was phrased by Karl Pearson back in 1897, affects all data that measures parts of some whole, such as percentages, proportions, ppm and ppb. Such measurements are present in all fields of science, ranging from geology, biology, environmental sciences, forensic sciences, medicine and hydrology. This book presents the history and development of compositional data analysis along with Aitchison's log-ratio approach. Compositional Data Analysis describes the state of the art both in theoretical fields as well as applications in the different fields of science. Key Features: Reflects the state-of-the-art in compositional data analysis. Gives an overview of the historical development of compositional data analysis, as well as basic concepts and procedures. Looks at advances in algebra and calculus on the simplex. Presents applications in different fields of science, including, genomics, ecology, biology, geochemistry, planetology, chemistry and economics. Explores connections to correspondence analysis and the Dirichlet distribution. Presents a summary of three available software packages for compositional data analysis. Supported by an accompanying website featuring R code. Applied scientists working on compositional data analysis in any field of science, both in academia and professionals will benefit from this book, along with graduate students in any field of science working with compositional data. |
| cite seq data analysis: Clustering Stability Ulrike Von Luxburg, 2010 A popular method for selecting the number of clusters is based on stability arguments: one chooses the number of clusters such that the corresponding clustering results are most stable. In recent years, a series of papers has analyzed the behavior of this method from a theoretical point of view. However, the results are very technical and difficult to interpret for non-experts. In this paper we give a high-level overview about the existing literature on clustering stability. In addition to presenting the results in a slightly informal but accessible way, we relate them to each other and discuss their different implications. |
| cite seq data analysis: Biological Sequence Analysis Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison, 1998-04-23 Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field. |
| cite seq data analysis: Statistical Genomics Ewy Mathé, Sean Davis, 2016-03-24 This volume expands on statistical analysis of genomic data by discussing cross-cutting groundwork material, public data repositories, common applications, and representative tools for operating on genomic data. Statistical Genomics: Methods and Protocols is divided into four sections. The first section discusses overview material and resources that can be applied across topics mentioned throughout the book. The second section covers prominent public repositories for genomic data. The third section presents several different biological applications of statistical genomics, and the fourth section highlights software tools that can be used to facilitate ad-hoc analysis and data integration. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible analysis protocols, and tips on troubleshooting and avoiding known pitfalls. Through and practical, Statistical Genomics: Methods and Protocols, explores a range of both applications and tools and is ideal for anyone interested in the statistical analysis of genomic data. |
| cite seq data analysis: Next Generation Sequencing Jerzy Kulski, 2016-01-14 Next generation sequencing (NGS) has surpassed the traditional Sanger sequencing method to become the main choice for large-scale, genome-wide sequencing studies with ultra-high-throughput production and a huge reduction in costs. The NGS technologies have had enormous impact on the studies of structural and functional genomics in all the life sciences. In this book, Next Generation Sequencing Advances, Applications and Challenges, the sixteen chapters written by experts cover various aspects of NGS including genomics, transcriptomics and methylomics, the sequencing platforms, and the bioinformatics challenges in processing and analysing huge amounts of sequencing data. Following an overview of the evolution of NGS in the brave new world of omics, the book examines the advances and challenges of NGS applications in basic and applied research on microorganisms, agricultural plants and humans. This book is of value to all who are interested in DNA sequencing and bioinformatics across all fields of the life sciences. |
| cite seq data analysis: Tumor Immunology and Immunotherapy - Cellular Methods Part B , 2020-01-28 Tumor Immunology and Immunotherapy - Cellular Methods Part B, Volume 632, the latest release in the Methods in Enzymology series, continues the legacy of this premier serial with quality chapters authored by leaders in the field. Topics covered include Quantitation of calreticulin exposure associated with immunogenic cell death, Side-by-side comparisons of flow cytometry and immunohistochemistry for detection of calreticulin exposure in the course of immunogenic cell death, Quantitative determination of phagocytosis by bone marrow-derived dendritic cells via imaging flow cytometry, Cytofluorometric assessment of dendritic cell-mediated uptake of cancer cell apoptotic bodies, Methods to assess DC-dependent priming of T cell responses by dying cells, and more. |
| cite seq data analysis: The Mouse Nervous System Charles Watson, George Paxinos, Luis Puelles, 2011-11-28 The Mouse Nervous System provides a comprehensive account of the central nervous system of the mouse. The book is aimed at molecular biologists who need a book that introduces them to the anatomy of the mouse brain and spinal cord, but also takes them into the relevant details of development and organization of the area they have chosen to study. The Mouse Nervous System offers a wealth of new information for experienced anatomists who work on mice. The book serves as a valuable resource for researchers and graduate students in neuroscience. Systematic consideration of the anatomy and connections of all regions of the brain and spinal cord by the authors of the most cited rodent brain atlases A major section (12 chapters) on functional systems related to motor control, sensation, and behavioral and emotional states A detailed analysis of gene expression during development of the forebrain by Luis Puelles, the leading researcher in this area Full coverage of the role of gene expression during development and the new field of genetic neuroanatomy using site-specific recombinases Examples of the use of mouse models in the study of neurological illness |
| cite seq data analysis: Bioinformatics and Computational Biology Solutions Using R and Bioconductor Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit, 2005-12-29 Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers. |
| cite seq data analysis: Advances in methods and tools for multi-omics data analysis Ornella Cominetti, Sergio Oller Moreno, Sumeet Agarwal, 2023-05-12 |
| cite seq data analysis: Single Cell Methods Valentina Proserpio, 2019 This volume provides a comprehensive overview for investigating biology at the level of individual cells. Chapters are organized into eight parts detailing a single-cell lab, single cell DNA-seq, RNA-seq, single cell proteomic and epigenetic, single cell multi-omics, single cell screening, and single cell live imaging. 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, Single Cell Methods: Sequencing and Proteomics aims to make each experiment easily reproducible in every lab. |
| cite seq data analysis: Flow Cytometry and Cell Sorting Andreas Radbruch, 2013-03-14 The analysis and sorting of large numbers of cells with a fluorescence-activated cell sorter (FACS) was first achieved some 30 years ago. Since then, this technology has been rapidly developed and is used today in many laboratories. A Springer Lab Manual Review of the First Edition: This is a most useful volume which will be a welcome addition for personal use and also for laboratories in a wide range of disciplines. Highly recommended. CYTOBIOS |
| cite seq data analysis: Machine Learning in Single-Cell RNA-seq Data Analysis Khalid Raza, |
| cite seq data analysis: Single-cell analysis on the pathophysiology of autoimmune diseases Shiang-Jong Tzeng, InKyeom Kim , Kuang-Hui Sun, 2024-07-11 Despite increasing research to facilitate the understanding of the pathophysiology of autoimmune disorders, the exact cause of the incident of autoimmunity is unknown. Current concepts on the occurrence of autoimmune diseases are thought to involve autoantigens, genetic predisposition, disease triggers, and the breakdown of immune tolerance. In addition to the breakdown of immunological tolerance, one key characteristic of autoimmune disease is that within a single disease there is considerable variability in the clinical manifestation and severity in patients. Single-cell omics have emerged as an effective means of unraveling the complexity and heterogeneity of chronic disease development and therapeutic responses. Recently, advances in cutting-edge spatial profiling of diverse cell types have increased our understanding of how distinct cells interact and orchestrate at specific locations across a tissue landscape in both physiological and pathological contexts at the single-cell level. |
| cite seq data analysis: Applications of RNA-Seq in Biology and Medicine Irina Vlasova-St. Louis, 2021-10-13 This book evaluates and comprehensively summarizes the scientific findings that have been achieved through RNA-sequencing (RNA-Seq) technology. RNA-Seq transcriptome profiling of healthy and diseased tissues allows FOR understanding the alterations in cellular phenotypes through the expression of differentially spliced RNA isoforms. Assessment of gene expression by RNA-Seq provides new insight into host response to pathogens, drugs, allergens, and other environmental triggers. RNA-Seq allows us to accurately capture all subtypes of RNA molecules, in any sequenced organism or single-cell type, under different experimental conditions. Merging genomics and transcriptomic profiling provides novel information underlying causative DNA mutations. Combining RNA-Seq with immunoprecipitation and cross-linking techniques is a clever multi-omics strategy assessing transcriptional, post-transcriptional and post-translational levels of gene expression regulation. |
| cite seq data analysis: Next Steps for Functional Genomics National Academies of Sciences, Engineering, and Medicine, Division on Earth and Life Studies, Board on Life Sciences, 2020-12-18 One of the holy grails in biology is the ability to predict functional characteristics from an organism's genetic sequence. Despite decades of research since the first sequencing of an organism in 1995, scientists still do not understand exactly how the information in genes is converted into an organism's phenotype, its physical characteristics. Functional genomics attempts to make use of the vast wealth of data from -omics screens and projects to describe gene and protein functions and interactions. A February 2020 workshop was held to determine research needs to advance the field of functional genomics over the next 10-20 years. Speakers and participants discussed goals, strategies, and technical needs to allow functional genomics to contribute to the advancement of basic knowledge and its applications that would benefit society. This publication summarizes the presentations and discussions from the workshop. |
| cite seq data analysis: ADKAR Jeff Hiatt, 2006 In his first complete text on the ADKAR model, Jeff Hiatt explains the origin of the model and explores what drives each building block of ADKAR. Learn how to build awareness, create desire, develop knowledge, foster ability and reinforce changes in your organization. The ADKAR Model is changing how we think about managing the people side of change, and provides a powerful foundation to help you succeed at change. |
| cite seq data analysis: Applications of RNA-Seq and Omics Strategies Fabio Marchi, Priscila Cirillo, Elvis Cueva Mateo, 2017-09-13 The large potential of RNA sequencing and other omics techniques has contributed to the production of a huge amount of data pursuing to answer many different questions that surround the science's great unknowns. This book presents an overview about powerful and cost-efficient methods for a comprehensive analysis of RNA-Seq data, introducing and revising advanced concepts in data analysis using the most current algorithms. A holistic view about the entire context where transcriptome is inserted is also discussed here encompassing biological areas with remarkable technological advances in the study of systems biology, from microorganisms to precision medicine. |
| cite seq data analysis: Transcriptome Analysis Miroslav Blumenberg, 2019-11-20 Transcriptome analysis is the study of the transcriptome, of the complete set of RNA transcripts that are produced under specific circumstances, using high-throughput methods. Transcription profiling, which follows total changes in the behavior of a cell, is used throughout diverse areas of biomedical research, including diagnosis of disease, biomarker discovery, risk assessment of new drugs or environmental chemicals, etc. Transcriptome analysis is most commonly used to compare specific pairs of samples, for example, tumor tissue versus its healthy counterpart. In this volume, Dr. Pyo Hong discusses the role of long RNA sequences in transcriptome analysis, Dr. Shinichi describes the next-generation single-cell sequencing technology developed by his team, Dr. Prasanta presents transcriptome analysis applied to rice under various environmental factors, Dr. Xiangyuan addresses the reproductive systems of flowering plants and Dr. Sadovsky compares codon usage in conifers. |
| cite seq data analysis: Transcriptome and Single-Cell Sequencing Analyses to Classify Immune Subtypes, Uncover Novel Biomarkers, and Assess Immunotherapeutic Responses in Cancer Hongda Liu, Jie Shen, Zheng Gong, Xianzhou Song , Peixin Dong, 2024-07-24 According to the most recent projections of the International Agency for Research on Cancer (IARC), there would be around 19.3 million new cases of cancer and 10 million cancer-related deaths globally in 2022. Cancer research has never halted. In particular, research into the cancer immunological microenvironment is gaining popularity. |
| cite seq data analysis: Bayesian Inference for Gene Expression and Proteomics Kim-Anh Do, Peter Müller, Marina Vannucci, 2006-07-24 Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation. |
| cite seq data analysis: Interactive Web-Based Data Visualization with R, plotly, and shiny Carson Sievert, 2020-01-30 The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics. |
| cite seq data analysis: Fusarium wilt Jeffrey Coleman, 2021-10-23 This volume provides a collection of molecular protocols detailing the most common and modern techniques on fusarium wilt. Chapters guide readers through methods on initial isolation, molecular-based identification, genome characterization, generation of mutants, and characterization of interactions with other organisms including host plants. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Fusarium wilt: Methods and Protocols aims to be a valuable resource for mycologists, plant pathologists, microbiologists, geneticists, and other scientists that have an interest in members of the Fusarium oxysporum species complex or closely related fungi. |
| cite seq data analysis: Cancer Systems and Integrative Biology Usha N. Kasid, Robert Clarke, 2023-05-16 This thorough volume explores recent advances that have revolutionized the field of precision oncology. The chapters, contributed by experts in the areas of cancer systems and integrative biology, provide hands-on guidance toward developing tools to monitor spatial and temporal changes in tumors, tracking tumor markers in blood, and ultimately developing precision medicine to combat cancer in real time. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that ensures successful results. Authoritative and informative, Cancer Systems and Integrative Biology serves as an invaluable resource for researchers, pharmaceutical scientists, and oncologists interested in expanding their knowledge base in the current developments in cancer research. |
| cite seq data analysis: Optimal Bayesian Classification Lori A. Dalton, Edward R. Dougherty, 2019 The most basic problem of engineering is the design of optimal operators. Design takes different forms depending on the random process constituting the scientific model and the operator class of interest. This book treats classification, where the underlying random process is a feature-label distribution, and an optimal operator is a Bayes classifier, which is a classifier minimizing the classification error. With sufficient knowledge we can construct the feature-label distribution and thereby find a Bayes classifier. Rarely, do we possess such knowledge. On the other hand, if we had unlimited data, we could accurately estimate the feature-label distribution and obtain a Bayes classifier. Rarely do we possess sufficient data. The aim of this book is to best use whatever knowledge and data are available to design a classifier. The book takes a Bayesian approach to modeling the feature-label distribution and designs an optimal classifier relative to a posterior distribution governing an uncertainty class of feature-label distributions. In this way it takes full advantage of knowledge regarding the underlying system and the available data. Its origins lie in the need to estimate classifier error when there is insufficient data to hold out test data, in which case an optimal error estimate can be obtained relative to the uncertainty class. A natural next step is to forgo classical ad hoc classifier design and simply find an optimal classifier relative to the posterior distribution over the uncertainty class-this being an optimal Bayesian classifier-- |
| cite seq data analysis: RNA-seq Data Analysis Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong, 2014-09-19 The State of the Art in Transcriptome AnalysisRNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript le |
| cite seq data analysis: Computational Systems Biology in Medicine and Biotechnology Sonia Cortassa, Miguel A. Aon, 2022-05-23 This volume addresses the latest state-of-the-art systems biology-oriented approaches that--driven by big data and bioinformatics--are utilized by Computational Systems Biology, an interdisciplinary field that bridges experimental tools with computational tools to tackle complex questions at the frontiers of knowledge in medicine and biotechnology. The chapters in this book are organized into six parts: systems biology of the genome, epigenome, and redox proteome; metabolic networks; aging and longevity; systems biology of diseases; spatiotemporal patterns of rhythms, morphogenesis, and complex dynamics; and genome scale metabolic modeling in biotechnology. In every chapter, readers will find varied methodological approaches applied at different levels, from molecular, cellular, organ to organisms, genome to phenome, and health and disease. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics; criteria utilized for applying specific methodologies; lists of the necessary materials, reagents, software, databases, algorithms, mathematical models, and dedicated analytical procedures; step-by-step, readily reproducible laboratory, bioinformatics, and computational protocols all delivered in didactic and clear style and abundantly illustrated with express case studies and tutorials; and tips on troubleshooting and advice for achieving reproducibility while avoiding mistakes and misinterpretations. The overarching goal driving this volume is to excite the expert and stimulate the newcomer to the field of Computational Systems Biology. Cutting-edge and authoritative, Computational Systems Biology in Medicine and Biotechnology: Methods and Protocols is a valuable resource for pre- and post-graduate students in medicine and biotechnology, and in diverse areas ranging from microbiology to cellular and organismal biology, as well as computational and experimental biologists, and researchers interested in utilizing comprehensive systems biology oriented methods. |
| cite seq data analysis: Towards Precision Medicine for Immune-Mediated Disorders: Advances in Using Big Data and Artificial Intelligence to Understand Heterogeneity in Inflammatory Responses Xu-jie Zhou, Lam Cheung Tsoi, Amanda S. MacLeod, 2022-08-16 Topic Editor Dr. MacLeod is employed by Janssen. All other Topic Editors declare no competing interests with regards to the Research Topic subject. |
| cite seq data analysis: Neuroimmune Pharmacology Tsuneya Ikezu, Howard E. Gendelman, 2016-12-22 The second edition of Neuroimmune Pharmacology bridges the disciplines of neuroscience, immunology and pharmacology from the molecular to clinical levels with particular thought made to engage new research directives and clinical modalities. Bringing together the foremost field authorities from around the world, Neuroimmune Pharmacology will serve as an invaluable resource for the basic and applied scientists of the current decade and beyond. |
| cite seq data analysis: Single-cell Molecular Characterization for Improving Cancer Immunotherapy Qihui Shi, Wei Wei, Ziming Li, 2022-02-22 Topic Editor Qihui Shi is the scientific co-founder of JunHealth, a company aiming to developing single-cell sequencing technologies for clinical applications, and received research funding from BeiGene. |
| cite seq data analysis: Women in Science - Rheumatology 2021 Garifallia Sakellariou, Silvia Piantoni, 2022-11-07 |
| cite seq data analysis: Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics Abhishek Kumar, Ashutosh Kumar Dubey, Sreenatha G. Anavatti, Pramod Singh Rathore, 2022-03-09 In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity. Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges. This book is useful for research scholars and students involved in critical condition analysis and computation models. |
| cite seq data analysis: Computational Methods for Precision Oncology Alessandro Laganà, 2022-03-01 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. |
| cite seq data analysis: Single-Cell OMICs Analyses in Cardiovascular Diseases , 2024-05-14 Single-cell OMICs analyses have recently become one of the most promising tools to probe biology at the cellular level, in large part due to its ability to address issues beyond the bulk analysis – a window into cellular heterogeneity. The ability to profile transcriptomic, epigenomic, proteomics, and metabolomics at the single cell level including more recently the spatial information has enhanced our ability to understand interactions between biomolecules in different contexts leading to the discovery of specific cellular subpopulations as well as biological mechanisms underlying pathologies which may be amenable to therapeutic interventions. The scale and availability of a variety of technologies to measure intricate molecular details have provided an impetus to research in many disease areas, including cardiovascular medicine. |
| cite seq data analysis: Deciphering Phagocyte Functions across Different Species Katrin Kierdorf, Marc S. Dionne, Yi Feng, Efstathios G. Stamatiades, 2021-09-29 |
| cite seq data analysis: 10 Years of frontiers in genetics: Past discoveries, current challenges and future perspectives William C. Cho, Jordi Pérez-Tur, Rosalba Giugno, Mehdi Pirooznia, Kathleen Boris-Lawrie, Dov Greenbaum, Blanka Rogina, Mojgan Rastegar, Rui Henrique, Peng Xu, Joao Batista Teixeira da Rocha, 2023-06-02 |
| cite seq data analysis: Modulation of Human Immune Parameters by Anticancer Therapies Ulrich Sack, Attila Tarnok, Il-Kang Na, Frank Preijers, 2021-01-18 |
| cite seq data analysis: Flow Cytometry Protocols Teresa S. Hawley, |
| cite-seq data analysis: Computational Methods for Single-Cell Data Analysis Guo-Cheng Yuan, 2019-02-14 This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. 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, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis. |
| cite-seq data analysis: Next-Generation Sequencing Data Analysis Xinkun Wang, 2016-04-06 A Practical Guide to the Highly Dynamic Area of Massively Parallel SequencingThe development of genome and transcriptome sequencing technologies has led to a paradigm shift in life science research and disease diagnosis and prevention. Scientists are now able to see how human diseases and phenotypic changes are connected to DNA mutation, polymorphi |
| cite-seq data analysis: Statistical Genomics Ewy Mathé, Sean Davis, 2016-03-24 This volume expands on statistical analysis of genomic data by discussing cross-cutting groundwork material, public data repositories, common applications, and representative tools for operating on genomic data. Statistical Genomics: Methods and Protocols is divided into four sections. The first section discusses overview material and resources that can be applied across topics mentioned throughout the book. The second section covers prominent public repositories for genomic data. The third section presents several different biological applications of statistical genomics, and the fourth section highlights software tools that can be used to facilitate ad-hoc analysis and data integration. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible analysis protocols, and tips on troubleshooting and avoiding known pitfalls. Through and practical, Statistical Genomics: Methods and Protocols, explores a range of both applications and tools and is ideal for anyone interested in the statistical analysis of genomic data. |
| cite-seq data analysis: Compositional Data Analysis Vera Pawlowsky-Glahn, Antonella Buccianti, 2011-09-19 It is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more difficult to realize that so many statisticians and users of statistics are unaware of the particular problems affecting compositional data, as well as their solutions. The issue of ``spurious correlation'', as the situation was phrased by Karl Pearson back in 1897, affects all data that measures parts of some whole, such as percentages, proportions, ppm and ppb. Such measurements are present in all fields of science, ranging from geology, biology, environmental sciences, forensic sciences, medicine and hydrology. This book presents the history and development of compositional data analysis along with Aitchison's log-ratio approach. Compositional Data Analysis describes the state of the art both in theoretical fields as well as applications in the different fields of science. Key Features: Reflects the state-of-the-art in compositional data analysis. Gives an overview of the historical development of compositional data analysis, as well as basic concepts and procedures. Looks at advances in algebra and calculus on the simplex. Presents applications in different fields of science, including, genomics, ecology, biology, geochemistry, planetology, chemistry and economics. Explores connections to correspondence analysis and the Dirichlet distribution. Presents a summary of three available software packages for compositional data analysis. Supported by an accompanying website featuring R code. Applied scientists working on compositional data analysis in any field of science, both in academia and professionals will benefit from this book, along with graduate students in any field of science working with compositional data. |
| cite-seq data analysis: Handbook of Statistical Bioinformatics Henry Horng-Shing Lu, Bernhard Schölkopf, Martin T. Wells, Hongyu Zhao, 2022-12-08 Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology. |
| cite-seq data analysis: Clustering Stability Ulrike Von Luxburg, 2010 A popular method for selecting the number of clusters is based on stability arguments: one chooses the number of clusters such that the corresponding clustering results are most stable. In recent years, a series of papers has analyzed the behavior of this method from a theoretical point of view. However, the results are very technical and difficult to interpret for non-experts. In this paper we give a high-level overview about the existing literature on clustering stability. In addition to presenting the results in a slightly informal but accessible way, we relate them to each other and discuss their different implications. |
| cite-seq data analysis: Biological Sequence Analysis Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison, 1998-04-23 Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field. |
| cite-seq data analysis: Next Generation Sequencing Jerzy Kulski, 2016-01-14 Next generation sequencing (NGS) has surpassed the traditional Sanger sequencing method to become the main choice for large-scale, genome-wide sequencing studies with ultra-high-throughput production and a huge reduction in costs. The NGS technologies have had enormous impact on the studies of structural and functional genomics in all the life sciences. In this book, Next Generation Sequencing Advances, Applications and Challenges, the sixteen chapters written by experts cover various aspects of NGS including genomics, transcriptomics and methylomics, the sequencing platforms, and the bioinformatics challenges in processing and analysing huge amounts of sequencing data. Following an overview of the evolution of NGS in the brave new world of omics, the book examines the advances and challenges of NGS applications in basic and applied research on microorganisms, agricultural plants and humans. This book is of value to all who are interested in DNA sequencing and bioinformatics across all fields of the life sciences. |
| cite-seq data analysis: Applications of RNA-Seq and Omics Strategies Fabio Marchi, Priscila Cirillo, Elvis Cueva Mateo, 2017-09-13 The large potential of RNA sequencing and other omics techniques has contributed to the production of a huge amount of data pursuing to answer many different questions that surround the science's great unknowns. This book presents an overview about powerful and cost-efficient methods for a comprehensive analysis of RNA-Seq data, introducing and revising advanced concepts in data analysis using the most current algorithms. A holistic view about the entire context where transcriptome is inserted is also discussed here encompassing biological areas with remarkable technological advances in the study of systems biology, from microorganisms to precision medicine. |
| cite-seq data analysis: Tumor Immunology and Immunotherapy - Cellular Methods Part B , 2020-01-28 Tumor Immunology and Immunotherapy - Cellular Methods Part B, Volume 632, the latest release in the Methods in Enzymology series, continues the legacy of this premier serial with quality chapters authored by leaders in the field. Topics covered include Quantitation of calreticulin exposure associated with immunogenic cell death, Side-by-side comparisons of flow cytometry and immunohistochemistry for detection of calreticulin exposure in the course of immunogenic cell death, Quantitative determination of phagocytosis by bone marrow-derived dendritic cells via imaging flow cytometry, Cytofluorometric assessment of dendritic cell-mediated uptake of cancer cell apoptotic bodies, Methods to assess DC-dependent priming of T cell responses by dying cells, and more. |
| cite-seq data analysis: The Mouse Nervous System Charles Watson, George Paxinos, Luis Puelles, 2011-11-28 The Mouse Nervous System provides a comprehensive account of the central nervous system of the mouse. The book is aimed at molecular biologists who need a book that introduces them to the anatomy of the mouse brain and spinal cord, but also takes them into the relevant details of development and organization of the area they have chosen to study. The Mouse Nervous System offers a wealth of new information for experienced anatomists who work on mice. The book serves as a valuable resource for researchers and graduate students in neuroscience. Systematic consideration of the anatomy and connections of all regions of the brain and spinal cord by the authors of the most cited rodent brain atlases A major section (12 chapters) on functional systems related to motor control, sensation, and behavioral and emotional states A detailed analysis of gene expression during development of the forebrain by Luis Puelles, the leading researcher in this area Full coverage of the role of gene expression during development and the new field of genetic neuroanatomy using site-specific recombinases Examples of the use of mouse models in the study of neurological illness |
| cite-seq data analysis: Flow Cytometry and Cell Sorting Andreas Radbruch, 2013-03-14 The analysis and sorting of large numbers of cells with a fluorescence-activated cell sorter (FACS) was first achieved some 30 years ago. Since then, this technology has been rapidly developed and is used today in many laboratories. A Springer Lab Manual Review of the First Edition: This is a most useful volume which will be a welcome addition for personal use and also for laboratories in a wide range of disciplines. Highly recommended. CYTOBIOS |
| cite-seq data analysis: Bioinformatics and Computational Biology Solutions Using R and Bioconductor Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit, 2005-12-29 Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers. |
| cite-seq data analysis: Advances in methods and tools for multi-omics data analysis Ornella Cominetti, Sergio Oller Moreno, Sumeet Agarwal, 2023-05-12 |
| cite-seq data analysis: Machine Learning in Single-Cell RNA-seq Data Analysis Khalid Raza, |
| cite-seq data analysis: Transcriptome Analysis Miroslav Blumenberg, 2019-11-20 Transcriptome analysis is the study of the transcriptome, of the complete set of RNA transcripts that are produced under specific circumstances, using high-throughput methods. Transcription profiling, which follows total changes in the behavior of a cell, is used throughout diverse areas of biomedical research, including diagnosis of disease, biomarker discovery, risk assessment of new drugs or environmental chemicals, etc. Transcriptome analysis is most commonly used to compare specific pairs of samples, for example, tumor tissue versus its healthy counterpart. In this volume, Dr. Pyo Hong discusses the role of long RNA sequences in transcriptome analysis, Dr. Shinichi describes the next-generation single-cell sequencing technology developed by his team, Dr. Prasanta presents transcriptome analysis applied to rice under various environmental factors, Dr. Xiangyuan addresses the reproductive systems of flowering plants and Dr. Sadovsky compares codon usage in conifers. |
| cite-seq data analysis: Single Cell Methods Valentina Proserpio, 2019 This volume provides a comprehensive overview for investigating biology at the level of individual cells. Chapters are organized into eight parts detailing a single-cell lab, single cell DNA-seq, RNA-seq, single cell proteomic and epigenetic, single cell multi-omics, single cell screening, and single cell live imaging. 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, Single Cell Methods: Sequencing and Proteomics aims to make each experiment easily reproducible in every lab. |
| cite-seq data analysis: Single-cell analysis on the pathophysiology of autoimmune diseases Shiang-Jong Tzeng, InKyeom Kim , Kuang-Hui Sun, 2024-07-11 Despite increasing research to facilitate the understanding of the pathophysiology of autoimmune disorders, the exact cause of the incident of autoimmunity is unknown. Current concepts on the occurrence of autoimmune diseases are thought to involve autoantigens, genetic predisposition, disease triggers, and the breakdown of immune tolerance. In addition to the breakdown of immunological tolerance, one key characteristic of autoimmune disease is that within a single disease there is considerable variability in the clinical manifestation and severity in patients. Single-cell omics have emerged as an effective means of unraveling the complexity and heterogeneity of chronic disease development and therapeutic responses. Recently, advances in cutting-edge spatial profiling of diverse cell types have increased our understanding of how distinct cells interact and orchestrate at specific locations across a tissue landscape in both physiological and pathological contexts at the single-cell level. |
| cite-seq data analysis: Applications of RNA-Seq in Biology and Medicine Irina Vlasova-St. Louis, 2021-10-13 This book evaluates and comprehensively summarizes the scientific findings that have been achieved through RNA-sequencing (RNA-Seq) technology. RNA-Seq transcriptome profiling of healthy and diseased tissues allows FOR understanding the alterations in cellular phenotypes through the expression of differentially spliced RNA isoforms. Assessment of gene expression by RNA-Seq provides new insight into host response to pathogens, drugs, allergens, and other environmental triggers. RNA-Seq allows us to accurately capture all subtypes of RNA molecules, in any sequenced organism or single-cell type, under different experimental conditions. Merging genomics and transcriptomic profiling provides novel information underlying causative DNA mutations. Combining RNA-Seq with immunoprecipitation and cross-linking techniques is a clever multi-omics strategy assessing transcriptional, post-transcriptional and post-translational levels of gene expression regulation. |
| cite-seq data analysis: Next Steps for Functional Genomics National Academies of Sciences, Engineering, and Medicine, Division on Earth and Life Studies, Board on Life Sciences, 2020-12-18 One of the holy grails in biology is the ability to predict functional characteristics from an organism's genetic sequence. Despite decades of research since the first sequencing of an organism in 1995, scientists still do not understand exactly how the information in genes is converted into an organism's phenotype, its physical characteristics. Functional genomics attempts to make use of the vast wealth of data from -omics screens and projects to describe gene and protein functions and interactions. A February 2020 workshop was held to determine research needs to advance the field of functional genomics over the next 10-20 years. Speakers and participants discussed goals, strategies, and technical needs to allow functional genomics to contribute to the advancement of basic knowledge and its applications that would benefit society. This publication summarizes the presentations and discussions from the workshop. |
| cite-seq data analysis: ADKAR Jeff Hiatt, 2006 In his first complete text on the ADKAR model, Jeff Hiatt explains the origin of the model and explores what drives each building block of ADKAR. Learn how to build awareness, create desire, develop knowledge, foster ability and reinforce changes in your organization. The ADKAR Model is changing how we think about managing the people side of change, and provides a powerful foundation to help you succeed at change. |
| cite-seq data analysis: Bayesian Inference for Gene Expression and Proteomics Kim-Anh Do, Peter Müller, Marina Vannucci, 2006-07-24 Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation. |
| cite-seq data analysis: Transcriptome and Single-Cell Sequencing Analyses to Classify Immune Subtypes, Uncover Novel Biomarkers, and Assess Immunotherapeutic Responses in Cancer Hongda Liu, Jie Shen, Zheng Gong, Xianzhou Song , Peixin Dong, 2024-07-24 According to the most recent projections of the International Agency for Research on Cancer (IARC), there would be around 19.3 million new cases of cancer and 10 million cancer-related deaths globally in 2022. Cancer research has never halted. In particular, research into the cancer immunological microenvironment is gaining popularity. |
| cite-seq data analysis: Interactive Web-Based Data Visualization with R, plotly, and shiny Carson Sievert, 2020-01-30 The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics. |
| cite-seq data analysis: Fusarium wilt Jeffrey Coleman, 2021-10-23 This volume provides a collection of molecular protocols detailing the most common and modern techniques on fusarium wilt. Chapters guide readers through methods on initial isolation, molecular-based identification, genome characterization, generation of mutants, and characterization of interactions with other organisms including host plants. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Fusarium wilt: Methods and Protocols aims to be a valuable resource for mycologists, plant pathologists, microbiologists, geneticists, and other scientists that have an interest in members of the Fusarium oxysporum species complex or closely related fungi. |
| cite-seq data analysis: Single Cell Analysis Tuhin Subhra Santra, Fan-Gang Tseng, 2021-06-02 Cells are the most fundamental building block of all living organisms. The investigation of any type of disease mechanism and its progression still remains challenging due to cellular heterogeneity characteristics and physiological state of cells in a given population. The bulk measurement of millions of cells together can provide some general information on cells, but it cannot evolve the cellular heterogeneity and molecular dynamics in a certain cell population. Compared to this bulk or the average measurement of a large number of cells together, single-cell analysis can provide detailed information on each cell, which could assist in developing an understanding of the specific biological context of cells, such as tumor progression or issues around stem cells. Single-cell omics can provide valuable information about functional mutation and a copy number of variations of cells. Information from single-cell investigations can help to produce a better understanding of intracellular interactions and environmental responses of cellular organelles, which can be beneficial for therapeutics development and diagnostics purposes. This Special Issue is inviting articles related to single-cell analysis and its advantages, limitations, and future prospects regarding health benefits. |
| cite-seq data analysis: Optimal Bayesian Classification Lori A. Dalton, Edward R. Dougherty, 2019 The most basic problem of engineering is the design of optimal operators. Design takes different forms depending on the random process constituting the scientific model and the operator class of interest. This book treats classification, where the underlying random process is a feature-label distribution, and an optimal operator is a Bayes classifier, which is a classifier minimizing the classification error. With sufficient knowledge we can construct the feature-label distribution and thereby find a Bayes classifier. Rarely, do we possess such knowledge. On the other hand, if we had unlimited data, we could accurately estimate the feature-label distribution and obtain a Bayes classifier. Rarely do we possess sufficient data. The aim of this book is to best use whatever knowledge and data are available to design a classifier. The book takes a Bayesian approach to modeling the feature-label distribution and designs an optimal classifier relative to a posterior distribution governing an uncertainty class of feature-label distributions. In this way it takes full advantage of knowledge regarding the underlying system and the available data. Its origins lie in the need to estimate classifier error when there is insufficient data to hold out test data, in which case an optimal error estimate can be obtained relative to the uncertainty class. A natural next step is to forgo classical ad hoc classifier design and simply find an optimal classifier relative to the posterior distribution over the uncertainty class-this being an optimal Bayesian classifier-- |
| cite-seq data analysis: Cancer Systems and Integrative Biology Usha N. Kasid, Robert Clarke, 2023-05-16 This thorough volume explores recent advances that have revolutionized the field of precision oncology. The chapters, contributed by experts in the areas of cancer systems and integrative biology, provide hands-on guidance toward developing tools to monitor spatial and temporal changes in tumors, tracking tumor markers in blood, and ultimately developing precision medicine to combat cancer in real time. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that ensures successful results. Authoritative and informative, Cancer Systems and Integrative Biology serves as an invaluable resource for researchers, pharmaceutical scientists, and oncologists interested in expanding their knowledge base in the current developments in cancer research. |
| cite-seq data analysis: RNA-seq Data Analysis Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong, 2014-09-19 The State of the Art in Transcriptome AnalysisRNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript le |
| cite-seq data analysis: Neuroimmune Pharmacology Tsuneya Ikezu, Howard E. Gendelman, 2016-12-22 The second edition of Neuroimmune Pharmacology bridges the disciplines of neuroscience, immunology and pharmacology from the molecular to clinical levels with particular thought made to engage new research directives and clinical modalities. Bringing together the foremost field authorities from around the world, Neuroimmune Pharmacology will serve as an invaluable resource for the basic and applied scientists of the current decade and beyond. |
| cite-seq data analysis: Computational Systems Biology in Medicine and Biotechnology Sonia Cortassa, Miguel A. Aon, 2022-05-23 This volume addresses the latest state-of-the-art systems biology-oriented approaches that--driven by big data and bioinformatics--are utilized by Computational Systems Biology, an interdisciplinary field that bridges experimental tools with computational tools to tackle complex questions at the frontiers of knowledge in medicine and biotechnology. The chapters in this book are organized into six parts: systems biology of the genome, epigenome, and redox proteome; metabolic networks; aging and longevity; systems biology of diseases; spatiotemporal patterns of rhythms, morphogenesis, and complex dynamics; and genome scale metabolic modeling in biotechnology. In every chapter, readers will find varied methodological approaches applied at different levels, from molecular, cellular, organ to organisms, genome to phenome, and health and disease. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics; criteria utilized for applying specific methodologies; lists of the necessary materials, reagents, software, databases, algorithms, mathematical models, and dedicated analytical procedures; step-by-step, readily reproducible laboratory, bioinformatics, and computational protocols all delivered in didactic and clear style and abundantly illustrated with express case studies and tutorials; and tips on troubleshooting and advice for achieving reproducibility while avoiding mistakes and misinterpretations. The overarching goal driving this volume is to excite the expert and stimulate the newcomer to the field of Computational Systems Biology. Cutting-edge and authoritative, Computational Systems Biology in Medicine and Biotechnology: Methods and Protocols is a valuable resource for pre- and post-graduate students in medicine and biotechnology, and in diverse areas ranging from microbiology to cellular and organismal biology, as well as computational and experimental biologists, and researchers interested in utilizing comprehensive systems biology oriented methods. |
| cite-seq data analysis: MHC Class-I Loss and Cancer Immune Escape Federico Garrido, 2019-05-28 This book is about the escape strategies used by cancer cells to avoid the immune response of the host. The main characters of this story are the “Antigen Presenting Molecules” and the “T Lymphocytes”. The former are known as the Major Histocompatibility Complex (MHC): the H-2 and the HLA molecules. The latter are a subgroup of white cells travelling all over our body which are capable to distinguish between “self and non self”. Readers will know from the inside about the history of the HLA genetic system and will discover how T lymphocytes recognize and destroy cancer cells. One of the key important questions is: Why tumors arise, develop and metastasize? This book tries to answer this question and will explain how cancer cells become invisible to killer T lymphocytes. The loss of the HLA molecules is a major player in this tumor escape mechanism. Cancer immunotherapy is aimed at stimulating T lymphocytes to destroy tumor cells. However, the clinical response rate is not as high as expected. The molecular mechanisms responsible for MHC/HLA antigen loss play a crucial role in this resistance to immunotherapy. This immune escape mechanism will be discussed in different types of tumors: lung, prostate, bladder and breast...ect. as well as melanoma and lymphoma. This book will be useful to Oncologists, Pathologists and Immunologist that will enter this fascinating area of research. It will be also interesting for biologist, doctoral students and medical residents interested in “Tumor Immunology”. |
| cite-seq data analysis: Towards Precision Medicine for Immune-Mediated Disorders: Advances in Using Big Data and Artificial Intelligence to Understand Heterogeneity in Inflammatory Responses Xu-jie Zhou, Lam Cheung Tsoi, Amanda S. MacLeod, 2022-08-16 Topic Editor Dr. MacLeod is employed by Janssen. All other Topic Editors declare no competing interests with regards to the Research Topic subject. |
| cite-seq data analysis: Immune and Autoimmune Mechanisms in Cardiovascular Disease Dennis Wolf, Holger Winkels, Norbert Gerdes, Florian Kahles, Partha Dutta, Chiara Giannarelli, 2023-02-08 |
| cite-seq data analysis: Pediatric Cancer Immunotherapy Pouya Faridi, Nicholas Vitanza, Orazio Vittorio, 2022-09-22 |
| cite-seq data analysis: Single-cell Molecular Characterization for Improving Cancer Immunotherapy Qihui Shi, Wei Wei, Ziming Li, 2022-02-22 Topic Editor Qihui Shi is the scientific co-founder of JunHealth, a company aiming to developing single-cell sequencing technologies for clinical applications, and received research funding from BeiGene. |
| cite-seq data analysis: Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics Abhishek Kumar, Ashutosh Kumar Dubey, Sreenatha G. Anavatti, Pramod Singh Rathore, 2022-03-09 In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity. Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges. This book is useful for research scholars and students involved in critical condition analysis and computation models. |
| cite-seq data analysis: Women in Science - Rheumatology 2021 Garifallia Sakellariou, Silvia Piantoni, 2022-11-07 |
| cite-seq data analysis: Computational Methods for Precision Oncology Alessandro Laganà, 2022-03-01 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. |
| cite-seq data analysis: Single-Cell OMICs Analyses in Cardiovascular Diseases , 2024-05-14 Single-cell OMICs analyses have recently become one of the most promising tools to probe biology at the cellular level, in large part due to its ability to address issues beyond the bulk analysis – a window into cellular heterogeneity. The ability to profile transcriptomic, epigenomic, proteomics, and metabolomics at the single cell level including more recently the spatial information has enhanced our ability to understand interactions between biomolecules in different contexts leading to the discovery of specific cellular subpopulations as well as biological mechanisms underlying pathologies which may be amenable to therapeutic interventions. The scale and availability of a variety of technologies to measure intricate molecular details have provided an impetus to research in many disease areas, including cardiovascular medicine. |
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