Download or read book Predictive Modeling of Drug Sensitivity written by Ranadip Pal and published by Academic Press. This book was released on 2016-11-15 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive Modeling of Drug Sensitivity gives an overview of drug sensitivity modeling for personalized medicine that includes data characterizations, modeling techniques, applications, and research challenges. It covers the major mathematical techniques used for modeling drug sensitivity, and includes the requisite biological knowledge to guide a user to apply the mathematical tools in different biological scenarios. This book is an ideal reference for computer scientists, engineers, computational biologists, and mathematicians who want to understand and apply multiple approaches and methods to drug sensitivity modeling. The reader will learn a broad range of mathematical and computational techniques applied to the modeling of drug sensitivity, biological concepts, and measurement techniques crucial to drug sensitivity modeling, how to design a combination of drugs under different constraints, and the applications of drug sensitivity prediction methodologies. - Applies mathematical and computational approaches to biological problems - Covers all aspects of drug sensitivity modeling, starting from initial data generation to final experimental validation - Includes the latest results on drug sensitivity modeling that is based on updated research findings - Provides information on existing data and software resources for applying the mathematical and computational tools available

Download or read book Predictive Modeling of Pharmaceutical Unit Operations written by Preetanshu Pandey and published by Woodhead Publishing. This book was released on 2016-09-26 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of modeling and simulation tools is rapidly gaining prominence in the pharmaceutical industry covering a wide range of applications. This book focuses on modeling and simulation tools as they pertain to drug product manufacturing processes, although similar principles and tools may apply to many other areas. Modeling tools can improve fundamental process understanding and provide valuable insights into the manufacturing processes, which can result in significant process improvements and cost savings. With FDA mandating the use of Quality by Design (QbD) principles during manufacturing, reliable modeling techniques can help to alleviate the costs associated with such efforts, and be used to create in silico formulation and process design space. This book is geared toward detailing modeling techniques that are utilized for the various unit operations during drug product manufacturing. By way of examples that include case studies, various modeling principles are explained for the nonexpert end users. A discussion on the role of modeling in quality risk management for manufacturing and application of modeling for continuous manufacturing and biologics is also included. - Explains the commonly used modeling and simulation tools - Details the modeling of various unit operations commonly utilized in solid dosage drug product manufacturing - Practical examples of the application of modeling tools through case studies - Discussion of modeling techniques used for a risk-based approach to regulatory filings - Explores the usage of modeling in upcoming areas such as continuous manufacturing and biologics manufacturingBullet points

Download or read book Protein Kinase Inhibitors as Sensitizing Agents for Chemotherapy written by and published by Academic Press. This book was released on 2018-11-21 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tyrosine Kinase Inhibitors as Sensitizing Agents for Chemotherapy, the fourth volume in the Cancer Sensitizing Agents for Chemotherapy Series, focuses on strategic combination therapies that involve a variety of tyrosine kinase inhibitors working together to overcome multi-drug resistance in cancer cells. The book discusses several tyrosine kinase inhibitors that have been used as sensitizing agents, such as EGFR, BCR-ABL, ALK and BRAF. In each chapter, readers will find comprehensive knowledge on the inhibitor and its action, including its biochemical, genetic, and molecular mechanisms' emphases. This book is a valuable source for oncologists, cancer researchers and those interested in applying new sensitizing agents to their research in clinical practice and in trials. - Summarizes the sensitizing role of some tyrosine kinase inhibitors in existing research - Brings recent findings in several cancer types, both experimental and clinically, with a particular emphases on underlying biochemical, genetic, and molecular mechanisms - Provides an updated and comprehensive knowledge regarding the field of combinational cancer treatment

Download or read book Pacific Symposium on Biocomputing written by Russ B. Altman and published by World Scientific Publishing Company Incorporated. This book was released on 2013-11-19 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer panomics: Computational methods and infrastructure for integrative analysis of cancer high-throughput "OMICS" data. Session introduction / Soren Brunak ... [et al.] -- Tumor haplotype assembly algorithms for cancer genomics / Derek Aguiar, Wendy S.W. Wong, Sorin Istrail -- Extracting significant sample-specific cancer mutations using their protein interactions / Liviu Badea -- The stream algorithm: Computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity / Elias Chaibub Neto ... [et al.] -- Sharing information to reconstruct patient-specific pathways in heterogeneous diseases / Anthony Gitter ... [et al.] -- Detecting statistical interaction between somatic mutational events and germline variation from next-generation sequence data / Hao Hu, Chad D. Huff -- Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data / In Sock Jang ... [et al.] -- Integrative analysis of two cell lines derived from a non-small-lung cancer patient - A panomics approach / Oleg Mayba ... [et al.] -- An integrated approach to blood-based cancer diagnosis and biomarker discovery / Martin Renqiang Min ... [et al.] -- Multiplex meta-analysis of medulloblastoma expression studies with external controls / Alexander A. Morgan ... [et al.] -- Computational approaches to drug repurposing and pharmacology. Session introduction / S. Joshua Swamidass ... [et al.] -- Challenges in secondary analysis of high throughput screening data / Aurora S. Blucher, Shannon K. McWeeney -- Drug intervention response predictions with paradigm (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance / Douglas Brubaker ... [et al.] -- Anti-infectious drug repurposing using an integrated chemical genomics and structural systems biology approach / Clara Ng ... [et al.] -- Drug-target interaction prediction by integrating chemical, genomic, functional and pharmacological data / Fan Yang, Jinbo Xu, Jianyang Zeng -- Prediction of off-target drug effects through data fusion / Emmanuel R. Yera, Ann E. Cleves, Ajay N. Jain -- Exploring the pharmacogenomics knowledge base (PharmGKB) for repositioning breast cancer drugs by leveraging web ontology language (OWL) and cheminformatics approaches / Qian Zhu ... [et al.] -- Detecting and characterizing pleiotropy: New methods for uncovering the connection between the complexity of genomic architecture and multiple phenotypes. Session introduction / Anna L. Tyler, Dana C. Crawford, Sarah A. Pendergrass -- Using the bipartite human phenotype network to reveal pleiotropy and epistasis beyond the gene / Christian Darabos, Samantha H. Harmon, Jason H. Moore -- Environment-wide association study (EWAS) for type 2 diabetes in the Marshfield personalized medicine research project biobank / Molly A. Hall ... [et al.] -- Dissection of complex gene expression using the combined analysis of pleiotropy and epistasis / Vivek M. Philip, Anna L. Tyler, Gregory W. Carter -- Personalized medicine: From genotypes and molecular phenotypes towards therapy. Session introduction / Jennifer Listgarten ... [et al.] -- PATH-SCAN: A reporting tool for identifying clinically actionable variants / Roxana Daneshjou ... [et al.] -- Imputation-based assessment of next generation rare exome variant arrays / Alicia R. Martin ... [et al.] -- Utilization of an EMR-biorepository to identify the genetic predictors of calcineurin-inhibitor toxicity in heart transplant recipients/ Matthew Oetjens ... [et al.] -- Robust reverse engineering of dynamic gene networks under sample size heterogeneity / Ankur P. Parikh, Wei Wu, Eric P. Xing -- Variant priorization and analysis incorporating problematic regions of the genome / Anil Patwardhan ... [et al.] -- Bags of words models of epitope sets: HIV viral load regression with counting grids / Alessandro Perina, Pietro Lovato, Nebojsa Jojic -- Joint association discovery and diagnosis of Alzheimer's disease by supervised heterogeneous multiview learning / Shandian Zhe ... [et al.] -- Text and data mining for biomedical discover. Session introduction / Graciela H. Gonzalez ... [et al.] -- Vector quantization kernels for the classification of protein sequences and structures / Wyatt T. Clark, Predrag Radivojac -- Combining Heterogenous data for prediction of disease related and pharmacogenes / Christopher S. Funk, Lawrence E. Hunter, K. Bretonnel Cohen -- A novel profile biomarker diagnosis for mass spectral proteomics / Henry Han -- Towards pathway curation through literature mining - A case study using PharmGKB / Ravikumar K.E., Kavishwar B. Wagholikar, Hongfang Liu -- Sparse generalized functional linear model for predicting remission status of depression patients / Yashu Liu ... [et al.] -- Development of a data-mining algorithm to identify ages at reproductive milestones in electronic medical records / Jennifer Malinowski, Eric Farber-Eger, Dana C. Crawford -- An efficient algorithm to integrate network and attribute data for gene function prediction / Shankar Vembu, Quaid Morris -- Matrix factorization-based data fusion for gene function prediction in Baker's yeast and slime mold / Marinka Zitnik, Blaz Zupan -- Workshops. Applications of bioinformatics to non-coding RNAs in the era of next-generation sequencing / Chao Cheng, Jason Moore, Casey Greene -- Building the next generation of quantitative biologists / Kristine A. Pattin ... [et al.] -- Uncovering the etiology of autism spectrum disorders: Genomics, bioinformatics, environment, data collection and exploration, and future possibilities / Sarah A. Pendergrass, Santhosh Girirajan, Scott Selleck

Download or read book Applied Predictive Modeling written by Max Kuhn and published by Springer Science & Business Media. This book was released on 2013-05-17 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Download or read book Machine Learning in Non Stationary Environments written by Masashi Sugiyama and published by MIT Press. This book was released on 2012-03-30 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

Download or read book Healthcare Risk Adjustment and Predictive Modeling written by Ian G. Duncan and published by ACTEX Publications. This book was released on 2011 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is listed on the Course of Reading for SOA Fellowship study in the Group & Health specialty track. Healthcare Risk Adjustment and Predictive Modeling provides a comprehensive guide to healthcare actuaries and other professionals interested in healthcare data analytics, risk adjustment and predictive modeling. The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models. The second part of the book presents the concept of data mining and some of the common approaches used by modelers. The third and final section covers a number of predictive modeling and risk adjustment case-studies, with examples from Medicaid, Medicare, disability, depression diagnosis and provider reimbursement, as well as the use of predictive modeling and risk adjustment outside the U.S. For readers who wish to experiment with their own models, the book also provides access to a test dataset.

Download or read book Oncogenomics and Cancer Proteomics written by Cesar Lopez-Camarillo and published by BoD – Books on Demand. This book was released on 2013-03-13 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Oncogenomics and Cancer Proteomics - Novel Approaches in Biomarkers Discovery and Therapeutic Targets in Cancer presents comprehensive reviews of the most common cancers from bench to bedside applications by an international team of experts. This book will contribute to the scientific and medical community by providing up-to-date discoveries of oncogenomics and their potential applications in cancer translational research. It is intended for students, scientists, clinicians, oncologists and health professionals working in cancer research.

Download or read book Explanatory Model Analysis written by Przemyslaw Biecek and published by CRC Press. This book was released on 2021-02-15 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

Download or read book Drinking Water and Health Volume 8 written by National Research Council and published by National Academies Press. This book was released on 1987-02-01 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pharmacokinetics, the study of the movement of chemicals within the body, is a vital tool in assessing the risk of exposure to environmental chemicals. This bookâ€"a collection of papers authored by experts in academia, industry, and governmentâ€"reviews the progress of the risk-assessment process and discusses the role of pharmacokinetic principles in evaluating risk. In addition, the authors discuss software packages used to analyze data and to build models simulating biological phenomena. A summary chapter provides a view of trends in pharmacokinetic modeling and notes some prospective fields of study.

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

Download or read book Developing a Protocol for Observational Comparative Effectiveness Research A User s Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

Download or read book Biomarkers in Drug Development written by Michael R. Bleavins and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how biomarkers can boost the success rate of drug development efforts As pharmaceutical companies struggle to improve the success rate and cost-effectiveness of the drug development process, biomarkers have emerged as a valuable tool. This book synthesizes and reviews the latest efforts to identify, develop, and integrate biomarkers as a key strategy in translational medicine and the drug development process. Filled with case studies, the book demonstrates how biomarkers can improve drug development timelines, lower costs, facilitate better compound selection, reduce late-stage attrition, and open the door to personalized medicine. Biomarkers in Drug Development is divided into eight parts: Part One offers an overview of biomarkers and their role in drug development. Part Two highlights important technologies to help researchers identify new biomarkers. Part Three examines the characterization and validation process for both drugs and diagnostics, and provides practical advice on appropriate statistical methods to ensure that biomarkers fulfill their intended purpose. Parts Four through Six examine the application of biomarkers in discovery, preclinical safety assessment, clinical trials, and translational medicine. Part Seven focuses on lessons learned and the practical aspects of implementing biomarkers in drug development programs. Part Eight explores future trends and issues, including data integration, personalized medicine, and ethical concerns. Each of the thirty-eight chapters was contributed by one or more leading experts, including scientists from biotechnology and pharmaceutical firms, academia, and the U.S. Food and Drug Administration. Their contributions offer pharmaceutical and clinical researchers the most up-to-date understanding of the strategies used for and applications of biomarkers in drug development.

Download or read book Molecular Modeling of the Sensitivities of Energetic Materials written by Didier Mathieu and published by Elsevier. This book was released on 2022-04-01 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Molecular Modeling of the Sensitivities of Energetic Materials, Volume 22 introduces experimental aspects, explores the relationships between sensitivity, molecular structure and crystal structure, discusses insights from numerical simulations, and highlights applications of these approaches to the design of new materials. Providing practical guidelines for implementing predictive models and their application to the search for new compounds, this book is an authoritative guide to an exciting field of research that warrants a computer-aided approach for the investigation and design of safe and powerful explosives or propellants. Much recent effort has been put into modeling sensitivities, with most work focusing on impact sensitivity and leading to a lot of experimental data in this area. Models must therefore be developed to allow evaluation of significant properties from the structure of constitutive molecules. - Highlights a range of approaches for computational simulation and the importance of combining them to accurately understand or estimate different parameters - Provides an overview of experimental findings and knowledge in a quick and accessible format - Presents guidelines to implement sensitivity models using open-source python-related software, thus supporting easy implementation of flexible models and allowing fast assessment of hypotheses

Download or read book Drug Discovery Toxicology written by Yvonne Will and published by John Wiley & Sons. This book was released on 2016-03-22 with total page 899 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a guide for pharmaceutical professionals to the issues and practices of drug discovery toxicology, this book integrates and reviews the strategy and application of tools and methods at each step of the drug discovery process. • Guides researchers as to what drug safety experiments are both practical and useful • Covers a variety of key topics – safety lead optimization, in vitro-in vivo translation, organ toxicology, ADME, animal models, biomarkers, and –omics tools • Describes what experiments are possible and useful and offers a view into the future, indicating key areas to watch for new predictive methods • Features contributions from firsthand industry experience, giving readers insight into the strategy and execution of predictive toxicology practices

Download or read book Practical Applications of Computational Biology Bioinformatics 15th International Conference PACBB 2021 written by Miguel Rocha and published by Springer Nature. This book was released on 2021-08-27 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features novel research papers spanning many different subfields in bioinformatics and computational biology, presenting the latest research on the practical applications to promote fruitful interactions between young researchers in different areas related to the field. Clearly, biology is increasingly becoming a science of information, requiring tools from the computational sciences. To address these challenges, we have seen the emergence of a new generation of interdisciplinary scientists with a strong background in the biological and computational sciences. PACBB'21 expects to contribute to this effort by encouraging a successful collaboration of researchers in different areas related to bioinformatics. The PACBB'21 technical program included 17 papers covering many different subfields in bioinformatics and computational biology. Therefore, this conference, held in Salamanca (Spain), definitely promotes the collaboration of scientists from different research groups and with different backgrounds (computer scientists, mathematicians, biologists) to reach breakthrough solutions for these challenges.

Download or read book Molecular Interaction Fields written by Gabriele Cruciani and published by John Wiley & Sons. This book was released on 2006-05-12 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique reference source, edited by the world's most respected expert on molecular interaction field software, covers all relevant principles of the GRID force field and its applications in medicinal chemistry. Entire chapters on 3D-QSAR, pharmacophore searches, docking studies, metabolism predictions and protein selectivity studies, among others, offer a concise overview of this emerging field. As an added bonus, this handbook includes a CD-ROM with the latest commercial versions of the GRID program and related software.