| data science in cancer research: High-Dimensional Data Analysis in Cancer Research Xiaochun Li, Ronghui Xu, 2008-12-19 Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data. |
| data science in cancer research: Translational Biomedical Informatics Bairong Shen, Haixu Tang, Xiaoqian Jiang, 2016-10-31 This book introduces readers to essential methods and applications in translational biomedical informatics, which include biomedical big data, cloud computing and algorithms for understanding omics data, imaging data, electronic health records and public health data. The storage, retrieval, mining and knowledge discovery of biomedical big data will be among the key challenges for future translational research. The paradigm for precision medicine and healthcare needs to integratively analyze not only the data at the same level – e.g. different omics data at the molecular level – but also data from different levels – the molecular, cellular, tissue, clinical and public health level. This book discusses the following major aspects: the structure of cross-level data; clinical patient information and its shareability; and standardization and privacy. It offers a valuable guide for all biologists, biomedical informaticians and clinicians with an interest in Precision Medicine Informatics. |
| data science in cancer research: Machine Learning in Cancer Research with Applications in Colon Cancer and Big Data Analysis Zhongyu Lu, 2021 Cancer continues to be a growing problem as it is the foremost cause of death worldwide, killing millions of people each year. The number of people battling cancer continues to increase, owing to different reasons, such as lifestyle choices. Clinically, determining the cause of cancer is very challenging and often inaccurate. Incorporating efficient and accurate algorithms to detect cancer cases is becoming increasingly beneficial for scientists in computer science and healthcare, as well as a long-term benefit for doctors, patients, clinic practitioners, and more. Specifically, an automation of computation in machine learning could be a solution in the next generation of big data science technology. Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis presents algorithms that have been developed to evaluate big data approaches and cancer research. The chapters include artificial intelligence and machine learning approaches, as well as case studies to solve the predictive issues in colon cancer research. This book includes concepts and techniques used to run tasks in an automated manner with the intent to improve better accuracy in comparison with previous studies and methods. This book also covers the processes of research design, development, and outcome analytics in this field. Doctors, IT consultants, IT specialists, medical software professionals, data scientists, researchers, computer scientists, healthcare practitioners, academicians, and students can benefit from this critical resource. |
| data science in cancer research: Applying Big Data to Address the Social Determinants of Health in Oncology National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Health and Medicine Division, Board on Health Care Services, National Cancer Policy Forum, 2020-08-14 The National Academies of Sciences, Engineering, and Medicine held the workshop Applying Big Data to Address the Social Determinants of Health in Oncology on October 28â€29, 2019, in Washington, DC. This workshop examined social determinants of health (SDOH) in the context of cancer, and considered opportunities to effectively leverage big data to improve health equity and reduce disparities. The workshop featured presentations and discussion by experts in technology, oncology, and SDOH, as well as representatives from government, industry, academia, and health care systems. This publication summarizes the presentations and discussions from the workshop. |
| data science in cancer research: Making Data Talk David E. Nelson (M.D.), Bradford W. Hesse, Robert T. Croyle, 2009 The demand for health information continues to increase, but the ability of health professionals to provide it clearly remains variable. The aim of this book is (1) to summarize and synthesize research on the selection and presentation of data pertinent to public health, and (2) to provide practical suggestions, based on this research summary and synthesis, on how scientists and other public health practitioners can better communicate data to the public, policy makers, and the press in typical real-world situations. Because communication is complex and no one approach works for all audiences, the authors emphasize how to communicate data better (and in some instances, contrast this with how to communicate data worse), rather than attempting a cookbook approach. The book contains a wealth of case studies and other examples to illustrate major points, and actual situations whenever possible. Key principles and recommendations are summarized at the end of each chapter. This book will stimulate interest among public health practitioners, scholars, and students to more seriously consider ways they can understand and improve communication about data and other types of scientific information with the public, policy makers, and the press. Improved data communication will increase the chances that evidence-based scientific findings can play a greater role in improving the public's health. |
| data science in cancer research: Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning Rani, Geeta, Tiwari, Pradeep Kumar, 2020-10-16 By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students. |
| data science in cancer research: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| data science in cancer research: Sentiment Analysis and Knowledge Discovery in Contemporary Business Rajput, Dharmendra Singh, Thakur, Ramjeevan Singh, Basha, S. Muzamil, 2018-08-31 In the era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through online collaborative media. However, conducting sentiment analysis on these platforms can be challenging, especially for business professionals who are using them to collect vital data. Sentiment Analysis and Knowledge Discovery in Contemporary Business is an essential reference source that discusses applications of sentiment analysis as well as data mining, machine learning algorithms, and big data streams in business environments. Featuring research on topics such as knowledge retrieval and knowledge updating, this book is ideally designed for business managers, academicians, business professionals, researchers, graduate-level students, and technology developers seeking current research on data collection and management to drive profit. |
| data science in cancer research: Statistics for Health Data Science Ruth Etzioni, Micha Mandel, Roman Gulati, 2021-01-04 Students and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science. This textbook is designed to overcome students’ anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engaging explanations and examples. In this way, the authors cultivate a deep (“organic”) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts. This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms. Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/ |
| data science in cancer research: Data Analytics in Bioinformatics Rabinarayan Satpathy, Tanupriya Choudhury, Suneeta Satpathy, Sachi Nandan Mohanty, Xiaobo Zhang, 2021-01-20 Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more. |
| data science in cancer research: The Analysis of Case-control Studies Norman E. Breslow, N. E. Day, 1980 |
| data science in cancer research: Immunoepidemiology Peter J. Krause, Paula B. Kavathas, Nancy H. Ruddle, 2019-10-24 This textbook focuses on the nascent field of Immunoepidemiology that addresses how differences in immune responses among individuals affect the epidemiology of infectious diseases, cancer, hypersensitivity, and autoimmunity. The idea for the book originated from a course entitled “Immunology for Epidemiologists“ at the Yale School of Public Health. While many fine textbooks are available that address the immunological responses of individuals to pathogens, these provided very little information regarding how immunological variation among populations affects the epidemiology of disease. And yet, it has long been recognized that there is great immunologic diversity among people, which can have a profound effect on the epidemiology of disease. Careful review of the immunologic and epidemiologic literature revealed that there have been relatively few publications concerning immunoepidemiology and that no textbook is available on the subject. This textbook therefore aims to fill this void by providing a much-needed tool to comprehensively and efficiently teach immunoepidemiology. The book includes a section on the basic principles of immunology, and then applies them to particular examples of disease in human populations. The target audience for this text book are Masters of Public Health students. Others who should also find it of interest include PhD students in epidemiology, immunology, medical students, generalists, and specialists in immunology, infectious diseases, cancer, and rheumatology. |
| data science in cancer research: Advancing the Science of Cancer in Latinos Amelie G. Ramirez, Edward J. Trapido, 2019-11-21 This open access book gives an overview of the sessions, panel discussions, and outcomes of the Advancing the Science of Cancer in Latinos conference, held in February 2018 in San Antonio, Texas, USA, and hosted by the Mays Cancer Center and the Institute for Health Promotion Research at UT Health San Antonio. Latinos – the largest, youngest, and fastest-growing minority group in the United States – are expected to face a 142% rise in cancer cases in coming years. Although there has been substantial advancement in cancer prevention, screening, diagnosis, and treatment over the past few decades, addressing Latino cancer health disparities has not nearly kept pace with progress. The diverse and dynamic group of speakers and panelists brought together at the Advancing the Science of Cancer in Latinos conference provided in-depth insights as well as progress and actionable goals for Latino-focused basic science research, clinical best practices, community interventions, and what can be done by way of prevention, screening, diagnosis, and treatment of cancer in Latinos. These insights have been translated into the chapters included in this compendium; the chapters summarize the presentations and include current knowledge in the specific topic areas, identified gaps, and top priority areas for future cancer research in Latinos. Topics included among the chapters: Colorectal cancer disparities in Latinos: Genes vs. Environment Breast cancer risk and mortality in women of Latin American origin Differential cancer risk in Latinos: The role of diet Overcoming barriers for Latinos on cancer clinical trials Es tiempo: Engaging Latinas in cervical cancer research Emerging policies in U.S. health care Advancing the Science of Cancer in Latinos proves to be an indispensable resource offering key insights into actionable targets for basic science research, suggestions for clinical best practices and community interventions, and novel strategies and advocacy opportunities to reduce health disparities in Latino communities. It will find an engaged audience among researchers, academics, physicians and other healthcare professionals, patient advocates, students, and others with an interest in the broad field of Latino cancer. |
| data science in cancer research: Deep Learning for Cancer Diagnosis Utku Kose, Jafar Alzubi, 2020-09-12 This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation. |
| data science in cancer research: Sharing Clinical Trial Data Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Responsible Sharing of Clinical Trial Data, 2015-04-20 Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients. |
| data science in cancer research: Deep Learning for the Life Sciences Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, 2019-04-10 Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working |
| data science in cancer research: Translational Research in Breast Cancer Dong-Young Noh, Wonshik Han, Masakazu Toi, 2021-05-13 This book describes recent advances in translational research in breast cancer and presents emerging applications of this research that promise to have meaningful impacts on diagnosis and treatment. It introduces ideas and materials derived from the clinic that have been brought to the bench for basic research, as well as findings that have been applied back to the bedside. Detailed attention is devoted to breast cancer biology and cell signaling pathways and to cancer stem cell and tumor heterogeneity in breast cancer. Various patient-derived research models are discussed, and a further focus is the role of biomarkers in precision medicine for breast cancer patients. Next-generation clinical research receives detailed attention, addressing the increasingly important role of big data in breast cancer research and a wide range of other emerging developments. An entire section is also devoted to the management of women with high-risk breast cancer. Translational Research in Breast Cancer will help clinicians and scientists to optimize their collaboration in order to achieve the common goal of conquering breast cancer. |
| data science in cancer research: Smart Business Problems and Analytical Hints in Cancer Research Zemelak Goraga, 2024-02-12 Smart Business Problems and Analytical Hints in Cancer Research is a pioneering exploration of the intersection between data science, artificial intelligence, machine learning, and oncology. Delving into 25 advanced questions derived from real-world cancer research scenarios, this book offers comprehensive guidelines on leveraging data-driven methodologies to address key challenges in the field. From genomic profiling and patient data integration to tumor heterogeneity analysis and immunotherapy optimization, each question presents a nuanced case study accompanied by practical solutions. Through integrative analysis and predictive modeling, readers gain insights into personalized treatment strategies, biomarker discovery, and therapeutic response prediction. With a focus on innovation and impact, this book equips researchers, clinicians, and data scientists with the tools and techniques necessary to navigate the complex landscape of cancer analytics. By harnessing the power of data science and AI, Smart Business Problems and Analytical Hints in Cancer Research promises to revolutionize the future of oncology and improve patient outcomes worldwide. |
| data science in cancer research: Tumor Models in Cancer Research Beverly A. Teicher, 2001-11-07 Beverly A. Teicher and a panel of leading experts comprehensively describe for the first time in many years the state-of-the-art in animal tumor model research. The wide array of models detailed form the basis for the selection of compounds and treatments that go into clinical testing of patients, and include syngeneic models, human tumor xenograft models, orthotopic models, metastatic models, transgenic models, and gene knockout models. Synthesizing many years experience with all the major in vivo models currently available for the study of malignant disease, Tumor Models in Cancer Research provides preclinical and clinical cancer researchers alike with a comprehensive guide to the selection of these models, their effective use, and the optimal interpretation of their results. |
| data science in cancer research: New Theory of Discriminant Analysis After R. Fisher Shuichi Shinmura, 2016-12-27 This is the first book to compare eight LDFs by different types of datasets, such as Fisher’s iris data, medical data with collinearities, Swiss banknote data that is a linearly separable data (LSD), student pass/fail determination using student attributes, 18 pass/fail determinations using exam scores, Japanese automobile data, and six microarray datasets (the datasets) that are LSD. We developed the 100-fold cross-validation for the small sample method (Method 1) instead of the LOO method. We proposed a simple model selection procedure to choose the best model having minimum M2 and Revised IP-OLDF based on MNM criterion was found to be better than other M2s in the above datasets. We compared two statistical LDFs and six MP-based LDFs. Those were Fisher’s LDF, logistic regression, three SVMs, Revised IP-OLDF, and another two OLDFs. Only a hard-margin SVM (H-SVM) and Revised IP-OLDF could discriminate LSD theoretically (Problem 2). We solved the defect of the generalized inverse matrices (Problem 3). For more than 10 years, many researchers have struggled to analyze the microarray dataset that is LSD (Problem 5). If we call the linearly separable model Matroska, the dataset consists of numerous smaller Matroskas in it. We develop the Matroska feature selection method (Method 2). It finds the surprising structure of the dataset that is the disjoint union of several small Matroskas. Our theory and methods reveal new facts of gene analysis. |
| data science in cancer research: The Design and Analysis of Cohort Studies Norman E. Breslow, N. E. Day, 1987 |
| data science in cancer research: The Digital Cell Stephen J. Royle, 2019 Cell biology is becoming an increasingly quantitative field, as technical advances mean researchers now routinely capture vast amounts of data. This handbook is an essential guide to the computational approaches, image processing and analysis techniques, and basic programming skills that are now part of the skill set of anyone working in the field-- |
| data science in cancer research: Guide to Security for Full Virtualization Technologies K. A. Scarfone, 2011 The purpose of SP 800-125 is to discuss the security concerns associated with full virtualization technologies for server and desktop virtualization, and to provide recommendations for addressing these concerns. Full virtualization technologies run one or more operating systems and their applications on top of virtual hardware. Full virtualization is used for operational efficiency, such as in cloud computing, and for allowing users to run applications for multiple operating systems on a single computer. |
| data science in cancer research: Oncology Informatics Bradford W. Hesse, David Ahern, Ellen Beckjord, 2016-03-17 Oncology Informatics: Using Health Information Technology to Improve Processes and Outcomes in Cancer Care encapsulates National Cancer Institute-collected evidence into a format that is optimally useful for hospital planners, physicians, researcher, and informaticians alike as they collectively strive to accelerate progress against cancer using informatics tools. This book is a formational guide for turning clinical systems into engines of discovery as well as a translational guide for moving evidence into practice. It meets recommendations from the National Academies of Science to reorient the research portfolio toward providing greater cognitive support for physicians, patients, and their caregivers to improve patient outcomes. Data from systems studies have suggested that oncology and primary care systems are prone to errors of omission, which can lead to fatal consequences downstream. By infusing the best science across disciplines, this book creates new environments of Smart and Connected Health. Oncology Informatics is also a policy guide in an era of extensive reform in healthcare settings, including new incentives for healthcare providers to demonstrate meaningful use of these technologies to improve system safety, engage patients, ensure continuity of care, enable population health, and protect privacy. Oncology Informatics acknowledges this extraordinary turn of events and offers practical guidance for meeting meaningful use requirements in the service of improved cancer care. Anyone who wishes to take full advantage of the health information revolution in oncology to accelerate successes against cancer will find the information in this book valuable. Presents a pragmatic perspective for practitioners and allied health care professionals on how to implement Health I.T. solutions in a way that will minimize disruption while optimizing practice goals Proposes evidence-based guidelines for designers on how to create system interfaces that are easy to use, efficacious, and timesaving Offers insight for researchers into the ways in which informatics tools in oncology can be utilized to shorten the distance between discovery and practice |
| data science in cancer research: An Introduction to Variational Autoencoders Diederik P. Kingma, Max Welling, 2019-11-12 An Introduction to Variational Autoencoders provides a quick summary for the of a topic that has become an important tool in modern-day deep learning techniques. |
| data science in cancer research: Trends and Innovations in Information Systems and Technologies Álvaro Rocha, Hojjat Adeli, Luís Paulo Reis, Sandra Costanzo, Irena Orovic, Fernando Moreira, 2020-05-17 This book gathers selected papers presented at the 2020 World Conference on Information Systems and Technologies (WorldCIST’20), held in Budva, Montenegro, from April 7 to 10, 2020. WorldCIST provides a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences with and challenges regarding various aspects of modern information systems and technologies. The main topics covered are A) Information and Knowledge Management; B) Organizational Models and Information Systems; C) Software and Systems Modeling; D) Software Systems, Architectures, Applications and Tools; E) Multimedia Systems and Applications; F) Computer Networks, Mobility and Pervasive Systems; G) Intelligent and Decision Support Systems; H) Big Data Analytics and Applications; I) Human–Computer Interaction; J) Ethics, Computers & Security; K) Health Informatics; L) Information Technologies in Education; M) Information Technologies in Radiocommunications; and N) Technologies for Biomedical Applications. |
| data science in cancer research: Advances in Computational and Bio-Engineering S. Jyothi, D. M. Mamatha, Suresh Chandra Satapathy, K. Srujan Raju, Margarita N. Favorskaya, 2020-07-19 This book gathers state-of-the-art research in computational engineering and bioengineering to facilitate knowledge exchange between various scientific communities. Computational engineering (CE) is a relatively new discipline that addresses the development and application of computational models and simulations often coupled with high-performance computing to solve complex physical problems arising in engineering analysis and design in the context of natural phenomena. Bioengineering (BE) is an important aspect of computational biology, which aims to develop and use efficient algorithms, data structures, and visualization and communication tools to model biological systems. Today, engineering approaches are essential for biologists, enabling them to analyse complex physiological processes, as well as for the pharmaceutical industry to support drug discovery and development programmes. |
| data science in cancer research: Deploying OpenStack Ken Pepple, 2011-08 OpenStack was created with the audacious goal of being the ubiquitous software choice for building public and private cloud infrastructures. In just over a year, it's become the most talked-about project in open source. This concise book introduces OpenStack's general design and primary software components in detail, and shows you how to start using it to build cloud infrastructures. If you're a developer, technologist, or system administrator familiar with cloud offerings such as Rackspace Cloud or Amazon Web Services, Deploying OpenStack shows you how to obtain and deploy OpenStack software in a few controlled scenarios. Learn about OpenStack Compute (known as “Nova”), OpenStack Object Store (Swift), and OpenStack Image Service (Glance) Understand common pitfalls in architecting, deploying, and implementing your cloud infrastructure with OpenStack Determine which version of the OpenStack code base best suits your deployment needs Define your deployment scenario and finalize key design choices Install Nova on a single node with either the StackOps distro or an Ubuntu package Be familiar with important configuration options and important administrative commands |
| data science in cancer research: Principles of Data Science Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau, 2020-07-08 This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice |
| data science in cancer research: 30-Second Data Science Liberty Vittert, 2020-09-29 30-Second Data Scienceis the quickest way to discover how data is a driving force not just in the big issues, such as climate change and healthcare, but in our daily lives. Data science is an entirely new discipline that encompasses a new era of information, from finding criminals to predicting epidemics. But there’s more to it than the vast quantities of information gathered by our computers, smartphones, and credit cards. Carefully compiled by experts in the field,30-Second Data Science covers the basic statistical principles that drive the algorithms, how data affects us in every way—science, society, business, pleasure—along with the ethical quandaries and its future promise of a better world. Each 30-Second entry details a different facet of data science in just 300 words and one picture, showing how the concept of bringing together different types of data, and using powerful computer programs to find patterns no human eye could spot, is already transforming our world. Exploring key ideas and featuring biographies of the people behind them, 30-Second Data Science explains clearly and concisely all you need to know about data science, from basics to ethics. The 30 Second series presents concise, informative guides to the most important topics which shape the world around us, presenting terms which are key to understanding the subject in 30 seconds, 300 words, and one image. |
| data science in cancer research: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data science in cancer research: Data Engineering and Data Science Kukatlapalli Pradeep Kumar, Aynur Unal, Vinay Jha Pillai, Hari Murthy, M. Niranjanamurthy, 2023-10-03 DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library. |
| data science in cancer research: Upper Tract Urothelial Carcinoma Shahrokh F. Shariat, Evanguelos Xylinas, 2014-09-13 Upper Tract Urothelial Carcinoma represents the first book of its kind to be dedicated solely to UTUC. It's aim is to improve understanding and eventually care of a disease that is greatly understudied and underappreciated, yet commonly dealt with by many medical and urologic oncologists. The volume features new data regarding genetic susceptibility, gene expression studies and causative factors; contemporary concepts and controversies regarding diagnosis and staging of UTUC; prediction tools and their value in treatment decisions within each disease stage and patient selection and treatment options such as endoscopic management, distal ureterectomy, radical nephroureterectomy and chemotherapy. Up-to-date information regarding boundaries of surgical resection, indication and extent of lymphadenectomy is covered as well as the role of perioperative/neoadjuvant chemotherapy in patients with high-risk UTUC. Upper Tract Urothelial Carcinoma will be of great value to all Urologists, Medical Oncologists and fellows in Urologic Oncology as well as upper level residents in training in Urology and Medical Oncology. |
| data science in cancer research: Leveraging Data Science for Global Health Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai, 2020-07-31 This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient. |
| data science in cancer research: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data science in cancer research: Data Science and Interdisciplinary Research: Recent Trends and Applications Brojo Kishore Mishra, 2023-09-27 Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields. Key Themes: Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting. Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis. Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity. Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture. Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities. Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments. Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face. Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis. Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data. Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics. |
| data science in cancer research: Systems Genetics Florian Markowetz, Michael Boutros, 2015-07-02 Whereas genetic studies have traditionally focused on explaining heritance of single traits and their phenotypes, recent technological advances have made it possible to comprehensively dissect the genetic architecture of complex traits and quantify how genes interact to shape phenotypes. This exciting new area has been termed systems genetics and is born out of a synthesis of multiple fields, integrating a range of approaches and exploiting our increased ability to obtain quantitative and detailed measurements on a broad spectrum of phenotypes. Gathering the contributions of leading scientists, both computational and experimental, this book shows how experimental perturbations can help us to understand the link between genotype and phenotype. A snapshot of current research activity and state-of-the-art approaches to systems genetics are provided, including work from model organisms such as Saccharomyces cerevisiae and Drosophila melanogaster, as well as from human studies. |
| data science in cancer research: Data Science for Everyone Fatih AKAY, 2023-03-20 Data Science for Everyone: A Beginner's Guide to Big Data and Analytics is a comprehensive guide for anyone interested in exploring the field of data science. Written in a user-friendly style, this book is designed to be accessible to readers with no prior background in data science. The book covers the fundamentals of data science and analytics, including data collection, data analysis, and data visualization. It also provides an overview of the most commonly used tools and techniques for working with big data. The book begins with an introduction to data science and its applications, followed by an overview of the different types of data and the challenges of working with them. The subsequent chapters delve into the main topics of data science, such as data exploration, data cleaning, data modeling, and data visualization, providing step-by-step instructions and practical examples to help readers master each topic. Throughout the book, the authors emphasize the importance of data ethics and responsible data management. They also cover the basics of machine learning, artificial intelligence, and deep learning, and their applications in data science. By the end of this book, readers will have a solid understanding of the key concepts and techniques used in data science, and will be able to apply them to real-world problems. Whether you are a student, a professional, or simply someone interested in the field of data science, this book is an essential resource for learning about the power and potential of big data and analytics. |
| data science in cancer research: Big Data in Oncology: Impact, Challenges, and Risk Assessment Neeraj Kumar Fuloria, Rishabha Malviya, Swati Verma, Balamurugan Balusamy, 2023-12-21 We are in the era of large-scale science. In oncology there is a huge number of data sets grouping information on cancer genomes, transcriptomes, clinical data, and more. The challenge of big data in cancer is to integrate all this diversity of data collected into a unique platform that can be analyzed, leading to the generation of readable files. The possibility of harnessing information from all the accumulated data leads to an improvement in cancer patient treatment and outcome. Solving the big data problem in oncology has multiple facets. Big data in Oncology: Impact, Challenges, and Risk Assessment brings together insights from emerging sophisticated information and communication technologies such as artificial intelligence, data science, and big data analytics for cancer management. This book focuses on targeted disease treatment using big data analytics. It provides information about targeted treatment in oncology, challenges and application of big data in cancer therapy. Recent developments in the fields of artificial intelligence, machine learning, medical imaging, personalized medicine, computing and data analytics for improved patient care. Description of the application of big data with AI to discover new targeting points for cancer treatment. Summary of several risk assessments in the field of oncology using big data. Focus on prediction of doses in oncology using big data The most targeted or relevant audience is academics, research scholars, health care professionals, hospital management, pharmaceutical chemists, the biomedical industry, software engineers and IT professionals. |
| data science in cancer research: Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide Agency for Health Care Research and Quality (U.S.), 2013-02-21 This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov) |
Data Science Opportunities for the National Cancer Institute
In May 2018, Dr. Norman Sharpless charged the Data Science Working Group to provide general guidance to the National Cancer Institute (NCI) on opportunities for NCI in data science, big …
Enhancing Observational Data Collection to Inform Precision …
In 2016, NCI’s Cancer MoonshotSM Blue Ribbon Panel emphasized the importance of data sharing and harmonization of results from diverse sources. In 2018, NIH released its first-ever …
NCI Cancer Research Data Commons
What is the NCI CRDC? NCI’s Cancer Research Data Commons (CRDC) is a cloud-based data science infrastructure that connects data sets with analytical tools to provide a foundation for …
Big data and artificial intelligence in cancer research - Cell …
research. We discuss key challenges in data curation and utilization for cancer re-search, offering strategic solutions. We detail the role and application of AI methodologies in processing …
Data science fast-tracks cancer drug development - Cognizant
We applied data science techniques to clinical trial data to make referencing cancer drugs and doses to various patient conditions faster and more accurate, smoothing the path to clinical …
Challenges and Opportunities: Integration of Data Science in …
The Data Science chapter in the context of cancer research is an essential component that has a major impact on the understanding and treatment of this disease. The application of Data...
Collaborating on Data, Science, and Infrastructure: The 20 …
The Cancer Research Network (CRN) is a consortium of 12 research groups, each affiliated with a nonprofit integrated health care delivery system, that was first funded in 1998. The overall goal …
Precision Medicine in Oncology: How Data Science is …
The Role of Data Science in Cancer Treatment Data science is foundational to precision oncology as it enables researchers to draw useful information from comprehensive and big volumes of …
Big data in basic and translational cancer research - Nature
There are five basic data types in cancer research: molec-ular omics data, perturbation phenotypic data, molecular interaction data, imaging data, and textual data.
THE BIG DATA SCIENTIST TRAINING ENHANCEMENT …
interdisciplinary researchers who use data science to make a difference in patient care. Fellows use computer science and mathematics to gain insights from healthcare data and solve real …
NCI Cancer Reserach Data Commons
cloud-based data science infrastructure that connects data sets with analytical tools, providing a foundation for the cancer research community to make new scientific discoveries and lower …
Beyond trust and reliability: reusing data in collaborative …
This qualitative study of post-doctoral researchers’ use of preexisting datasets investigates the practices of cancer-epidemiology post-docs working to understand their “Small Data” datasets.
Progress and potential: The Cancer Moonshot - Cell Press
In pursuit of the goal of accelerating cancer progress for patients, there are four essential features of all parts of the Moonshot: a strong commitment to collaborative research, an embrace of …
Artificial Intelligence (AI) for Improving Early Detection of …
Data Science and Artificial Intelligence (AI) Applications in Cancer Prevention Research and Care o Predictive models to estimate an individual's risk of developing cancer o Machine learning …
Leveraging real-world pancreatic cancer datasets to drive …
Here, we demonstrate the application of these datasets by providing a case study demonstrating how to combine and enrich data to accelerate pancreatic cancer research. Currently, the …
Deep Learning for Lung Cancer Detection: Tackling the …
Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and. nally assigns a cancer probability based on these results. We discuss the …
NCI Cancer Reserach Data Commons
NCI’s Cancer Research Data Commons (CRDC) is a cloud-based data science infrastructure that connects data sets with analytical tools, providing a foundation for the cancer research …
Next-Generation Liquid Biopsies: Embracing Data Science in …
Deeper and broader sequencing of circulating tumor DNA (ctDNA) has identi-fied a wealth of cancer markers in the circulation, resulting in a paradigm shift towards data science-driven …
THE BIG DATA SCIENTIST TRAINING ENHANCEMENT …
The Big Data Scientist Training Enhancement Program (BD-STEP) is a two-year fellowship program that uses data science to advance cancer research and patient care. A Veterans …
What is the NCI CRDC What is a Data Commons - Cancer
NCI’s Cancer Research Data Commons (CRDC) is a . cloud-based data science infrastructure that connects data sets with analytical tools, providing a foundation for the cancer research …
Data Science Opportunities for the National Cancer I…
In May 2018, Dr. Norman Sharpless charged the Data Science Working Group to provide general guidance …
Enhancing Observational Data Collection to Inform …
In 2016, NCI’s Cancer MoonshotSM Blue Ribbon Panel emphasized the importance of data sharing and …
NCI Cancer Research Data Commons
What is the NCI CRDC? NCI’s Cancer Research Data Commons (CRDC) is a cloud-based data science …
Big data and artificial intelligence in cancer rese…
research. We discuss key challenges in data curation and utilization for cancer re-search, offering strategic …
Data science fast-tracks cancer drug development
We applied data science techniques to clinical trial data to make referencing cancer drugs and doses to various …
