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See below for a selection of the latest books from Medical bioinformatics category. Presented with a red border are the Medical bioinformatics books that have been lovingly read and reviewed by the experts at Lovereading. With expert reading recommendations made by people with a passion for books and some unique features Lovereading will help you find great Medical bioinformatics books and those from many more genres to read that will keep you inspired and entertained. And it's all free!
With the increasing availability of omics data and mounting evidence of the usefulness of computational approaches to tackle multi-level data problems in bioinformatics and biomedical research in this post-genomics era, computational biology has been playing an increasingly important role in paving the way as basis for patient-centric healthcare.Two such areas are: (i) implementing AI algorithms supported by biomedical data would deliver significant benefits/improvements towards the goals of precision medicine (ii) blockchain technology will enable medical doctors to securely and privately build personal healthcare records, and identify the right therapeutic treatments and predict the progression of the diseases.A follow-up in the publication of our book Computation Methods with Applications in Bioinformatics Analysis (2017), topics in this volume include: clinical bioinformatics, omics-based data analysis, Artificial Intelligence (AI), blockchain, big data analytics, drug discovery, RNA-seq analysis, tensor decomposition and Boolean network.
MATLAB: A Practical Introduction to Programming and Problem Solving, winner of TAA's 2017 Textbook Excellence Award ( Texty ), guides the reader through both programming and built-in functions to easily exploit MATLAB's extensive capabilities for tackling engineering and scientific problems. Assuming no knowledge of programming, this book starts with programming concepts, such as variables, assignments, and selection statements, moves on to loops, and then solves problems using both the programming concept and the power of MATLAB. The fifth edition has been updated to reflect the functionality of the current version of MATLAB (R2018a), including the addition of local functions in scripts, the new string type, coverage of recently introduced functions to import data from web sites, and updates to the Live Editor and App Designer.
SAS Data Management for Public Health: An Introduction equips readers with the tools and knowledge they need to prepare public health data in SAS Data Management software for use in analysis. Highly accessible in nature, the book is specifically designed to help students who are new to SAS learn and master the system. The book is organized into 20 lessons. The opening lessons introduce SAS and provide tips and best practices for exploring data. Students are introduced to PROC MEANS, FREQ, UNIVARIATE, and PROC SGPLOT. They learn how to import data; merge, concatenate, and manage variables; perform data cleanup; and recode categorical and continuous variables. Specific lessons address comments, labels, and titles, formatting variables, conditional recoding, DO groups, arrays for recoding, and categorical data analysis. Closing lessons introduce stratified and subpopulation analysis, as well as logistic regression. The book includes an appendix to help students navigate and use SAS Studio. SAS Data Management for Public Health is an ideal resource for standalone courses in which SAS is taught or to complement any biostatistics or epidemiology course where students need to use SAS to analyze their data.
This book is designed to fill a current gap in the public health informatics literature by providing a resource that describes methods and best practices for conducting informatics projects in public health practice. It will provide detailed, practical approaches to the most common tasks required in public health informatics, supported by case studies when possible, and make available an online toolkit for readers to access as they venture forward to implement these approaches.
This book is intended to be an introductory bioinformatics textbook for mathematicians and computer scientists. It focuses on using algorithms and discrete mathematics to solve biological problems. The book systematically describes biological applications, the corresponding mathematical/computational problems, and various algorithmic solutions. It also discusses the practical use of various algorithmic methods and describes what algorithms should be used in different situations.
Neutrosophic Set in Medical Image Analysis gives an understanding of the concepts of NS, along with knowledge on how to gather, interpret, analyze and handle medical images using NS methods. It presents the latest cutting-edge research that gives insight into neutrosophic set's novel techniques, strategies and challenges, showing how it can be used in biomedical diagnoses systems. The neutrosophic set (NS), which is a generalization of fuzzy set, offers the prospect of overcoming the restrictions of fuzzy-based approaches to medical image analysis.
This third edition presents and dissects a wide variety of HIT failures so that the reader can understand in each case what went wrong and why and how to avoid such problems, without focusing on the involvement of specific people, organizations, or vendors. The lessons may be applied to future and existing projects, or used to understand why a previous project failed. The reader also learns how common causes of failure affect different kinds of HIT projects and with different results. Cases are organized by the type of focus (hospital care, ambulatory care, and community). Each case provides analysis by an author who was involved in the project plus the insight of an HIT expert. This book presents a model to discuss HIT failures in a safe and protected manner, providing an opportunity to focus on the lessons offered by a failed initiative as opposed to worrying about potential retribution for exposing a project as having failed. Access expert insight into key obstacles that must be overcome to leverage IT and transform healthcare. Each de-identified case study includes an analysis by a group of industry experts along with a counter analysis. Cases include a list of key words and are categorized by project (e.g. CPOE, business intelligence). Each case study concludes with a lesson learned section. Thought provoking commentary chapters add additional context to the challenges faced during HIT projects, from social and organizational to legal and contractual.
The first edition of Medical Bioinformatics and Biochemistry (Diabormatics) explains how medical biochemistry and bioinformatics could be used as a tool for analyzing the research data related to disease diagnosis and treatment. Bioinformatics is an interdisciplinary approach that includes concepts of biotechnology, microbiology, molecular biology, medicine and forensic science. This book is based on the recent development in the research dynamics of medical bioinformatics, biochemistry and progress in these fields. The book provides reference material for students of medical and life sciences. The development in genomic sequencing and in silico biology has provided the data needed to accomplish comparisons of derived nucleotide and protein sequences. The results of analysis may be used to formulate and test hypotheses about biochemical function. This first edition provides readers with a practical guide covering the full scope of concepts in medical bioinformatics and biochemistry related to diabetes. The basic purpose of this book is for students of medical and life sciences to understand the research methods of biochemistry and bioinformatics. This includes storing, receiving, and analyzing data from databases using various in silico tools. This book is a useful source of knowledge for MBBS, B.Sc, M.Sc / M.D. / M.S. and Ph.D level students looking for an accessible introduction to the subject.
The two-volume set LNBI 11465 and LNBI 11466 constitutes the proceedings of the 7th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2019, held in Granada, Spain, in May 2019. The total of 97 papers presented in the proceedings, was carefully reviewed and selected from 301 submissions. The papers are organized in topical sections as follows: Part I: High-throughput genomics: bioinformatics tools and medical applications; omics data acquisition, processing, and analysis; bioinformatics approaches for analyzing cancer sequencing data; next generation sequencing and sequence analysis; structural bioinformatics and function; telemedicine for smart homes and remote monitoring; clustering and analysis of biological sequences with optimization algorithms; and computational approaches for drug repurposing and personalized medicine. Part II: Bioinformatics for healthcare and diseases; computational genomics/proteomics; computational systems for modelling biological processes; biomedical engineering; biomedical image analysis; and biomedicine and e-health.
This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.