Mining Metabolic Networks and Biomedical Literature

With the advent of high-throughput experimental and genome technologies, the amounts of produced biological data and the related literature have increased dramatically. A significant portion of the produced biological data has revealed genotypic features of many model organisms. An outstanding problem presently is to map the characterized genotypic features to their phenotypic properties with the ultimate goal of making high-impact scientific discoveries in areas including diagnosing/curing diseases, engineering genomes, and inventing drugs. To this end, three major challenges concerning the management and analysis of the available data are: high volume (e.g., thousands of genes, millions of publications), increasing diversity (e.g., genes, pathways, metabolic profiles), and high complexity (e.g., hierarchical organization of entities, graph structures). In this book, we discuss several biological data mining and analysis problems. The book addresses distinct keystones on the path from genotype (e.g., genes and functionality annotations) to phenotype (e.g., metabolite profile changes). This book is intended for all Bioinformatics and Data Mining researchers, as well as instructors.

Autorentext

Ali Cakmak received his B.Sc. degree in Computer Engineering from Bilkent University, Turkey, and Ph.D. degree in Computer Science from Case Western Reserve University. His research interests are Bioinformatics, Data Mining, Systems Biology, Databases, and Knowledge Discovery. Presently, he is a member of the query optimizer group at Oracle, Inc.