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Programming --- Computational Biology --- Bioinformatics --- Bio-informatique --- Bioinformatics. --- Computational Biology. --- Bioinformatica --- Bioinformatica. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Data processing --- Bioinformatique. --- Bioinformatique

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Suitable for advanced undergraduates and postgraduates, "Understanding Bioinformatics" provides a definitive guide to this vibrant and evolving discipline. This book takes a conceptual approach. It guides the reader from first principles through to an understanding of the computational techniques and the key algorithms. "Understanding Bioinformatics" is an invaluable companion for students from their first encounter with the subject through to more advanced studies. This book is divided into seven parts, with the opening part introducing the basics of nucleic acids, proteins and databases. Subsequent parts are divided into 'Applications' and 'Theory' Chapters, allowing readers to focus their attention effectively.In each section, the Applications Chapter provides a fast and straightforward route to understanding the main concepts and 'getting started'. Each of these is then followed by Theory Chapters which give greater detail and present the underlying mathematics. In Part 2, Sequence Alignments, the Applications Chapter shows the reader how to get started on producing and analyzing sequence alignments, and using sequences for database searching, while the next two chapters look closely at the more advanced techniques and the mathematical algorithms involved. The Part 3 covers evolutionary processes and shows how bioinformatics can be used to help build phylogenetic trees. Part 4 looks at the characteristics of whole genomes. In Parts 5 and 6 the focus turns to secondary and tertiary structure - predicting structural conformation and analysing structure-function relationships. The last part surveys methods of analyzing data from a set of genes or proteins of an organism and is rounded off with an overview of systems biology.The writing style of "Understanding Bioinformatics" is notable for its clarity, while the extensive, full-color artwork has been designed to present the key concepts with simplicity and consistency. Each chapter uses mind-maps and flow diagrams to Give an overview of the conceptual links within each topic.

Biomathematics. Biometry. Biostatistics --- Computational Biology --- Bioinformatics --- Bio-informatique --- methods --- Bioinformatics. --- methods. --- Bioinformatica --- 57.087 --- Methods and techniques for parameter estimation. Recording of biological data --- Bioinformatica. --- 57.087 Methods and techniques for parameter estimation. Recording of biological data --- Methods. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Data processing

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Bioinformatics and Functional Genomics provides a broad-based introduction to bioinformatics by following three real-world examples throughout the book: retinol-binding protein, breast cancer, and a calcium binding site C2. The author emphasizes the use of computational tools and databases to study connections between the structure of proteins and genes to function, development, evolution, and disease. Readers learn real skills, such as how to analyze genes and proteins, how to make trees using phylogenetic software, how to extract data, and how to identify genes and proteins implicated in diseases.

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The book provides an undergraduate (final year) introduction to bioinformatics focussing on two key areas, genomics and protein sequence analysis. It provides an overview of primary, composite and secondary databases, and gives a brief introduction to the Internet and the world wide web.

Molecular biology --- Computer. Automation --- Bioinformatics --- Bio-informatique --- Biologie moléculaire --- Data processing --- Informatique --- Gene mapping --- Data processing. --- Biologie moléculaire --- Basic Sciences. Bioinformatics -- Bioinformatics (General) --- ALLW. --- 57.087 --- 57.087 Methods and techniques for parameter estimation. Recording of biological data --- Methods and techniques for parameter estimation. Recording of biological data --- Molecular biochemistry --- Molecular biophysics --- Biochemistry --- Biophysics --- Biomolecules --- Systems biology --- Molecular biology - Data processing. --- Gene mapping - Data processing. --- Basic Sciences. Bioinformatics -- Bioinformatics (General). --- Computational Biology

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A visit to a physician these days is cold: physicians spend most of their time typing at computers, making minimal eye contact. Appointments generally last only a few minutes, with scarce time for the doctor to connect to a patient's story, or explain how and why different procedures and treatments might be undertaken. As a result, errors abound: indeed, misdiagnosis is the fourth-leading cause of death in the United States, trailing only heart disease, cancer, and stroke. This is because, despite having access to more resources than ever, doctors are vulnerable not just to the economic demand to see more patients, but to distraction, burnout, data overload, and their own intrinsic biases. Physicians are simply overmatched. As Eric Topol argues in Deep Medicine, artificial intelligence can help. Natural-language processing could automatically record notes from our doctor visits; virtual psychiatrists could better predict the risk of suicide or other mental health issues for vulnerable patients; deep-learning software will make every physician a master diagnostician; and we could even use smartphone apps to take our own medical selfies for skin exams and receive immediate analysis. . On top of that, the virtual smartphone assistants of today--Alexa, Siri, Cortana--could analyze our daily health data to reduce the need for doctor visits and trips to the emergency room, and support for people suffering from asthma, epilepsy, and heart disease. By integrating tools like these into their daily medical practice, doctors would be able to spend less time collecting and cataloging information, and more time providing thorough, intimate, and meaningful care for their patients, as no machine can. Artificial intelligence can also help remedy the debilitating cost of healthcare, both for individuals and the economy writ large. The medical sector now absorbs 20 percent of the US gross domestic product--it is largest sector by dollars and jobs. And it's very inefficient. Take the cost of medical scans: There are over 20 million medical scans performed in the US every day, and an MRI, for example, costs hundreds to thousands of dollars. AI could process 260 million medical scans (more than 2 weeks' worth) in less than 24 hours for a cost of only $1000. We pay billions and billions of dollars for the same work today. The American health care system needs a serious reboot, and artificial intelligence is just the thing to press the restart button. As innovative as it is hopeful, Deep Medicine ultimately shows us how we can leverage artificial intelligence for better care at lower costs with more empathy, for the benefit of patients and physicians alike.

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