Bøker i Science, Engineering, And Biology Informatics-serien

Mapping the genomic landscapes is one of the most exciting frontiers of science. This book provides an in-depth presentation of some of the important computational biology approaches to genomic sequence analysis. It discusses methods for discovering patterns in DNA and RNA.

Aims to provide a treatise, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. This book brings together research articles from scientists around the world.

Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains.

Introduces an interdisciplinary set of methods and references on novel techniques from artificial intelligence, data mining, engineering, pattern recognition, and ontological data mining fields that are applicable to bioinformatics. This book explains the novel approaches and summarizes their advantages and disadvantages.

Covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. This book introduces widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. It also introduces bioinformatics research methods.

Intends to present innovative approaches from database researchers supporting the challenging process of knowledge discovery in biomedicine. This book is suitable for students and researchers in informatics who are keen to contribute to this emerging field of interdisciplinary research.

Focuses on the development and application of the advanced data mining, machine learning, and visualization techniques for the identification of significant patterns in gene expression microarray data. This book is suitable for biomedical researchers for learning the methods for analyzing gene expression microarray data.