Predictive Modeling Of Drug Sensitivity

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

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

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

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.

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

This volume covers a wide variety of state of the art cancer-related methods and tools for data analysis and interpretation. Chapters were designed to attract a broad readership, ranging from active researchers in computational biology and bioinformatics developers, clinical oncologists, and anti-cancer drug developers wishing to rationalize their search for new compounds. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, installation instructions for computational tools discussed, explanations of the input and output formats, and illustrative examples of applications. Authoritative and cutting-edge, Cancer Bioinformatics: Methods and Protocols aims to support researchers performing computational analysis of cancer-related data.

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.

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.