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Biomarkers in Inflammatory Bowel Diseases
This book provides a comprehensive and complete overview of biomarkers in clinical practice for inflammatory bowel disease (IBD) bringing together the literature in a clear and concise manner. The book bridges the gap between growing knowledge at the bench and current and future applications of biomarkers in clinical practice. The central structure of the book focuses on prognostic and predictive biomarkers in IBD with an emphasis on the fields of research and scientific techniques (genomics, proteomics and metabonomics) that have led to biomarker discovery and places these biomarkers within a clinical context to help understand their utility in clinical practice. This book will be of use to...
Chemical and Functional Genomic Approaches to Stem Cell Biology and Regenerative Medicine
Scientists believe that stem cells have the potential to revolutionize the treatment of numerous diseases and conditions. This guide covers recent advances in technologies and their applications in stem cell biology, addressing the use of both embryonic and adult stem cells and discussing diverse technologies, including genome-wide expression analysis, informatics, chemical genomics, and more. Applications covered encompass self-renewal, differentiation, reprogramming, and regeneration in model organisms. This is a premier reference for practicing professionals involved in stem cell research and students.
Directory of Graduate Research
Faculties, publications and doctoral theses in departments or divisions of chemistry, chemical engineering, biochemistry and pharmaceutical and/or medicinal chemistry at universities in the United States and Canada.
Representation Learning for Natural Language Processing
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Biomarkers and Immunotherapy of Hepatic-Biliary-Pancreatic Cancers
Hepatic-biliary-pancreatic cancers, mainly including hepatocellular carcinoma, cholangiocarcinoma, gallbladder cancer, are highly aggressive malignancies with few treatment options and poor prognoses. Effects of the traditional systemic treatments such as chemotherapy and radiotherapy, are quite limited to hepatic-biliary-pancreatic cancers. For the patients who suffer from hepatic-biliary-pancreatic cancer and lose the opportunity of radical surgery, few targeted drugs are available. Even for the drugs in clinical trial, potential drug targets and prognostic biomarkers for hepatic-biliary-pancreatic cancer are less studied compared to other common cancers such as lung cancer. Prognostic bio...
Introduction to Information Retrieval
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
