This open access book is a step-by-step introduction on how shell scripting can help solve many of the data processing tasks that Health and Life specialists face everyday with minimal software dependencies. The examples presented in the book show how simple command line tools can be used and combined to retrieve data and text from web resources, to filter and mine literature, and to explore the semantics encoded in biomedical ontologies. To store data this book relies on open standard text file formats, such as TSV, CSV, XML, and OWL, that can be open by any text editor or spreadsheet application. The first two chapters, Introduction and Resources, provide a brief introduction to the shell scripting and describe popular data resources in Health and Life Sciences. The third chapter, Data Retrieval, starts by introducing a common data processing task that involves multiple data resources. Then, this chapter explains how to automate each step of that task by introducing the required commands line tools one by one. The fourth chapter, Text Processing, shows how to filter and analyze text by using simple string matching techniques and regular expressions. The last chapter, Semantic Processing, shows how XPath queries and shell scripting is able to process complex data, such as the graphs used to specify ontologies. Besides being almost immutable for more than four decades and being available in most of our personal computers, shell scripting is relatively easy to learn by Health and Life specialists as a sequence of independent commands. Comprehending them is like conducting a new laboratory protocol by testing and understanding its procedural steps and variables, and combining their intermediate results. Thus, this book is particularly relevant to Health and Life specialists or students that want to easily learn how to process data and text, and which in return may facilitate and inspire them to acquire deeper bioinformatics skills in the future.
The potential misuse of advances in life sciences research is raising concerns about national security threats. Dual Use Research of Concern in the Life Sciences: Current Issues and Controversies examines the U.S. strategy for reducing biosecurity risks in life sciences research and considers mechanisms that would allow researchers to manage the dissemination of the results of research while mitigating the potential for harm to national security.
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.
The goal of this book is to make it easier for scientists, especially those new to scientific writing, to write about their results and to get their manuscripts accepted in peer-reviewed journals. The book covers each step throughout the submission process, from organizing and outlining the manuscript, presenting statistical data and results, to what happens during the in-house manuscript review process and what to do if an article is initially rejected. In addition to providing practical exercises on these topics, the book focuses on helping writers distil their research into concise take-home messages for readers, in order to convey information as clearly as possible to the target audience.
A guide to modern scanning electron microscopy instrumentation, methodology and techniques, highlighting novel applications to cell and molecular biology.
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
Biomedical advances have made it possible to identify and manipulate features of living organisms in useful ways-leading to improvements in public health, agriculture, and other areas. The globalization of scientific and technical expertise also means that many scientists and other individuals around the world are generating breakthroughs in the life sciences and related technologies. The risks posed by bioterrorism and the proliferation of biological weapons capabilities have increased concern about how the rapid advances in genetic engineering and biotechnology could enable the production of biological weapons with unique and unpredictable characteristics. Globalization, Biosecurity, and the Future of Life Sciences examines current trends and future objectives of research in public health, life sciences, and biomedical science that contain applications relevant to developments in biological weapons 5 to 10 years into the future and ways to anticipate, identify, and mitigate these dangers.