A revealing and provocative look at the current state of global science We take the advance of science as given. But how does science really work? Is it truly as healthy as we tend to think? How does the system itself shape what scientists do? The Secret Life of Science takes a clear-eyed and provocative look at the current state of global science, shedding light on a cutthroat and tightly tensioned enterprise that even scientists themselves often don't fully understand. The Secret Life of Science is a dispatch from the front lines of modern science. It paints a startling picture of a complex scientific ecosystem that has become the most competitive free-market environment on the planet. It reveals how big this ecosystem really is, what motivates its participants, and who reaps the rewards. Are there too few scientists in the world or too many? Are some fields expanding at the expense of others? What science is shared or published, and who determines what the public gets to hear about? What is the future of science? Answering these and other questions, this controversial book explains why globalization is not necessarily good for science, nor is the continued growth in the number of scientists. It portrays a scientific community engaged in a race for limited resources that determines whether careers are lost or won, whose research visions become the mainstream, and whose vested interests end up in control. The Secret Life of Science explains why this hypercompetitive environment is stifling the diversity of research and the resiliency of science itself, and why new ideas are needed to ensure that the scientific enterprise remains healthy and vibrant.

Since the discovery of the structure of DNA and the birth of the genetic age, a powerful vocabulary has emerged to express science’s growing command over the matter of life. Armed with knowledge of the code that governs all living things, biology and biotechnology are poised to edit, even rewrite, the texts of life to correct nature’s mistakes. Yet, how far should the capacity to manipulate what life is at the molecular level authorize science to define what life is for? This book looks at flash points in law, politics, ethics, and culture to argue that science’s promises of perfectibility have gone too far. Science may have editorial control over the material elements of life, but it does not supersede the languages of sense-making that have helped define human values across millennia: the meanings of autonomy, integrity, and privacy; the bonds of kinship, family, and society; and the place of humans in nature.

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.

Authoritative, thorough, and engaging, Life: The Science of Biology achieves an optimal balance of scholarship and teachability, never losing sight of either the science or the student. The first introductory text to present biological concepts through the research that revealed them, Life covers the full range of topics with an integrated experimental focus that flows naturally from the narrative. This approach helps to bring the drama of classic and cutting-edge research to the classroom - but always in the context of reinforcing core ideas and the innovative scientific thinking behind them. Students will experience biology not just as a litany of facts or a highlight reel of experiments, but as a rich, coherent discipline.

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