Download or read book Computational Life Sciences II written by Michael R. Berthold and published by Springer Science & Business Media. This book was released on 2006-09-21 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Symposium on Computational Life Sciences, CompLife 2006, held in Cambridge, UK, in September 2006.The 25 revised full papers presented were carefully reviewed and selected from 56 initial submissions. The papers are organized in topical sections on genomics, data mining, molecular simulation, molecular informatics, systems biology, biological networks/metabolism, and computational neuroscience.

Download or read book Handbook of Research on Computational Grid Technologies for Life Sciences Biomedicine and Healthcare written by Cannataro, Mario and published by IGI Global. This book was released on 2009-05-31 with total page 960 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides methodologies and developments of grid technologies applied in different fields of life sciences"--Provided by publisher.

Download or read book Chemical Information Mining written by Debra L. Banville and published by CRC Press. This book was released on 2008-12-15 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: The First Book to Describe the Technical and Practical Elements of Chemical Text MiningExplores the development of chemical structure extraction capabilities and how to incorporate these technologies in daily research workFor scientific researchers, finding too much information on a subject, not finding enough information, or not being able&nb

Download or read book Computational Systems Biology of Cancer written by Emmanuel Barillot and published by CRC Press. This book was released on 2012-08-25 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: The future of cancer research and the development of new therapeutic strategies rely on our ability to convert biological and clinical questions into mathematical models—integrating our knowledge of tumour progression mechanisms with the tsunami of information brought by high-throughput technologies such as microarrays and next-generation sequencing. Offering promising insights on how to defeat cancer, the emerging field of systems biology captures the complexity of biological phenomena using mathematical and computational tools. Novel Approaches to Fighting Cancer Drawn from the authors’ decade-long work in the cancer computational systems biology laboratory at Institut Curie (Paris, France), Computational Systems Biology of Cancer explains how to apply computational systems biology approaches to cancer research. The authors provide proven techniques and tools for cancer bioinformatics and systems biology research. Effectively Use Algorithmic Methods and Bioinformatics Tools in Real Biological Applications Suitable for readers in both the computational and life sciences, this self-contained guide assumes very limited background in biology, mathematics, and computer science. It explores how computational systems biology can help fight cancer in three essential aspects: Categorising tumours Finding new targets Designing improved and tailored therapeutic strategies Each chapter introduces a problem, presents applicable concepts and state-of-the-art methods, describes existing tools, illustrates applications using real cases, lists publically available data and software, and includes references to further reading. Some chapters also contain exercises. Figures from the text and scripts/data for reproducing a breast cancer data analysis are available at www.cancer-systems-biology.net.

Download or read book Data Mining in Drug Discovery written by Rémy D. Hoffmann and published by John Wiley & Sons. This book was released on 2013-09-25 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written for drug developers rather than computer scientists, this monograph adopts a systematic approach to mining scientifi c data sources, covering all key steps in rational drug discovery, from compound screening to lead compound selection and personalized medicine. Clearly divided into four sections, the first part discusses the different data sources available, both commercial and non-commercial, while the next section looks at the role and value of data mining in drug discovery. The third part compares the most common applications and strategies for polypharmacology, where data mining can substantially enhance the research effort. The final section of the book is devoted to systems biology approaches for compound testing. Throughout the book, industrial and academic drug discovery strategies are addressed, with contributors coming from both areas, enabling an informed decision on when and which data mining tools to use for one's own drug discovery project.

Download or read book Pharmaceutical Data Mining written by Konstantin V. Balakin and published by John Wiley & Sons. This book was released on 2009-11-19 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leading experts illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development In the era of post-genomic drug development, extracting and applying knowledge from chemical, biological, and clinical data is one of the greatest challenges facing the pharmaceutical industry. Pharmaceutical Data Mining brings together contributions from leading academic and industrial scientists, who address both the implementation of new data mining technologies and application issues in the industry. This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery—including chemogenomics, toxicogenomics, and individual drug response prediction. The five main sections of this volume cover: A general overview of the discipline, from its foundations to contemporary industrial applications Chemoinformatics-based applications Bioinformatics-based applications Data mining methods in clinical development Data mining algorithms, technologies, and software tools, with emphasis on advanced algorithms and software that are currently used in the industry or represent promising approaches In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and development. It is ideal for graduate-level courses covering pharmaceutical science, computational chemistry, and bioinformatics. In addition, it provides insight to pharmaceutical scientists, principal investigators, principal scientists, research directors, and all scientists working in the field of drug discovery and development and associated industries.

Download or read book Bioinformatics Research and Applications written by Ion Mandoiu and published by Springer. This book was released on 2007-08-06 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Symposium on Bioinformatics Research and Applications, ISBRA 2007, held in Atlanta, GA, USA in May 2007. The 55 revised full papers presented together with three invited talks cover a wide range of topics, including clustering and classification, gene expression analysis, gene networks, genome analysis, motif finding, pathways, protein structure prediction, protein domain interactions, phylogenetics, and software tools.

Download or read book Patenting Nanomedicines written by Eliana B. Souto and published by Springer Science & Business Media. This book was released on 2012-07-06 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Patenting Nanomedicines: Legal Aspects, Intellectual Property and Grant Opportunities” focusses on the fundamental aspects of Patenting Nanomedicines applied in different “Drug Delivery and Targeting Systems”. The promoters of new findings in this field of research are numerous and spread worldwide; therefore, managing intellectual property portfolios, and the acquisition and exploitation of new knowledge face several contingency factors. Today, the scientific community is discussing issues of economic outcomes in the field of Nanomedicines. Major concerns include questions as to whether the research groups, academics, industry and other stakeholders should work in unison or independently, if innovation or adaptation of new technology should be prioritized, public versus private research funding, and safeguarding versus sharing knowledge. However, despite its increasing importance for humankind, it is a matter of concern as to whether technological development can really be stimulated by patent protection. An intellectual property strategy should aim to develop a qualitative patent portfolio for continuous learning. This book addresses questions of ethics, socio-political policies and regulatory aspects of novel Nanomedicine-based products which are currently under development for the diagnosis and treatment of different types of diseases. It is divided in two parts – Part I is composed of the first 3 chapters, which focus on the “fundamentals” of legal aspects, emerging threats, advantages and disadvantages of patenting Nanomedicines, whereas Part II collects 12 chapters discussing different types of Nanomedicine-based products, their potential marketing aspects and patent protection. Whenever applied, each chapter offers a list of patents, based on a specific application in drug delivery and targeting. An outstanding team of 53 authors have contributed to this book, which will be of interest to professionals from the field of patent examiners, academics, researchers and scientists, students and other practitioners.

Download or read book Machine Learning in Biotechnology and Life Sciences written by Saleh Alkhalifa and published by Packt Publishing Ltd. This book was released on 2022-01-28 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key FeaturesLearn the applications of machine learning in biotechnology and life science sectorsDiscover exciting real-world applications of deep learning and natural language processingUnderstand the general process of deploying models to cloud platforms such as AWS and GCPBook Description The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time. You'll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data. By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP. What you will learnGet started with Python programming and Structured Query Language (SQL)Develop a machine learning predictive model from scratch using PythonFine-tune deep learning models to optimize their performance for various tasksFind out how to deploy, evaluate, and monitor a model in the cloudUnderstand how to apply advanced techniques to real-world dataDiscover how to use key deep learning methods such as LSTMs and transformersWho this book is for This book is for data scientists and scientific professionals looking to transcend to the biotechnology domain. Scientific professionals who are already established within the pharmaceutical and biotechnology sectors will find this book useful. A basic understanding of Python programming and beginner-level background in data science conjunction is needed to get the most out of this book.

Download or read book Personalized Medicine written by and published by Academic Press. This book was released on 2016-01-28 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Advances in Protein Chemistry and Structural Biology series is an essential resource for protein chemists. Each volume brings forth new information about protocols and analysis of proteins, with each thematically organized volume guest edited by leading experts in a broad range of protein-related topics. - Provides cutting-edge developments in protein chemistry and structural biology - Chapters are written by authorities in their field - Targeted to a wide audience of researchers, specialists, and students

Download or read book Computational Materials and Biological Sciences written by Kholmirzo Kholmurodov and published by Nova Science Publishers. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, original papers have been collected to demonstrate the efficient use of computer molecular dynamics simulation methods for the studying of nanoscale phenomena in the materials and life sciences. This book discusses modern molecular simulation methods for the study of molecular shape and properties in protein and polymer engineering, drugs and materials design, structure-function relationships, and related issues. This book contains the Proceedings of the MSSMBS-2014 and DSCMBS-2014 International Workshops which have been organised by the Joint Institute for Nuclear Research, the Institute of Bioorganic Chemistry of the Russian Academy of Sciences and S.U. Umarov Physical-Technical Institute of the Academy of Sciences of the Republic of Tajikistan. The research topics discussed in the MSSMBS'14 & DSCMBS'14 International Workshops are as follows: computer molecular simulation methods and approaches; molecular dynamics and Monte-Carlo techniques; modelling of biological molecules; physical and biochemical systems; material fabrication and design; drug design in medicine; computational and computing physics, chemistry, biology and medicine; GPU accelerated molecular dynamics and related techniques.

Download or read book Modeling in Systems Biology written by Ina Koch and published by Springer Science & Business Media. This book was released on 2010-10-21 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emerging, multi-disciplinary field of systems biology is devoted to the study of the relationships between various parts of a biological system, and computer modeling plays a vital role in the drive to understand the processes of life from an holistic viewpoint. Advancements in experimental technologies in biology and medicine have generated an enormous amount of biological data on the dependencies and interactions of many different molecular cell processes, fueling the development of numerous computational methods for exploring this data. The mathematical formalism of Petri net theory is able to encompass many of these techniques. This essential text/reference presents a comprehensive overview of cutting-edge research in applications of Petri nets in systems biology, with contributions from an international selection of experts. Those unfamiliar with the field are also provided with a general introduction to systems biology, the foundations of biochemistry, and the basics of Petri net theory. Further chapters address Petri net modeling techniques for building and analyzing biological models, as well as network prediction approaches, before reviewing the applications to networks of different biological classification. Topics and features: investigates the modular, qualitative modeling of regulatory networks using Petri nets, and examines an Hybrid Functional Petri net simulation case study; contains a glossary of the concepts and notation used in the book, in addition to exercises at the end of each chapter; covers the topological analysis of metabolic and regulatory networks, the analysis of models of signaling networks, and the prediction of network structure; provides a biological case study on the conversion of logical networks into Petri nets; discusses discrete modeling, stochastic modeling, fuzzy modeling, dynamic pathway modeling, genetic regulatory network modeling, and quantitative analysis techniques; includes a Foreword by Professor Jens Reich, Professor of Bioinformatics at Humboldt University and Max Delbrück Center for Molecular Medicine in Berlin. This unique guide to the modeling of biochemical systems using Petri net concepts will be of real utility to researchers and students of computational biology, systems biology, bioinformatics, computer science, and biochemistry.

Download or read book Mathematical Reviews written by and published by . This book was released on 2007 with total page 804 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Analysis for the Life Sciences with R written by Rafael A. Irizarry and published by CRC Press. This book was released on 2016-10-04 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book A Primer for Computational Biology written by Shawn T. O'Neil and published by . This book was released on 2017-12-21 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the "natural environment" of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful "pipe" operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.

Download or read book Handbook of Research on Computational and Systems Biology written by Limin Angela Liu and published by IGI Global. This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book offers information on the state-of-the-art development in the fields of computational biology and systems biology, presenting methods, tools, and applications of these fields by many leading experts around the globe"--Provided by publisher.

Download or read book Deep Learning for the Life Sciences written by Bharath Ramsundar and published by O'Reilly Media. This book was released on 2019-04-10 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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