PLoS Computational Biology: Translational Bioinformatics
http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11

I define “translational bioinformatics” research as the development and application of informatics methods that connect molecular entities to clinical entities.

In this collection, Dr. Kann and colleagues have assembled a wonderful group of authors to introduce the key threads of translational bioinformatics to those new to the field. The collection first provides a conceptual overview of the key data and concepts in the field, and then introduces some of the key methods for informatics discovery and applications. Just by examining the table of contents on the collection page (http://www.ploscollections.org/translati​onalbioinformatics), it is clear that many exciting and emerging health topics are squarely within the scope of translational bioinformatics: cancer, pharmacogenomics, medical genetics, small molecule drugs, and diseases of protein malfunction. There is an unmistakable flavor of personalized medicine here as well (genome association studies, mining genetic markers, personal genomic data analysis, data mining of electronic records): our molecular and clinical data resources are now allowing us to consider individual variations, and not simply population averages. I congratulate the editors and authors on creating an important collection of articles, and welcome the reader to an exciting field whose challenges and promise are unbounded.

Introduction to Translational Bioinformatics Collection
Chapter 1: Biomedical Knowledge Integration
Chapter 2: Data-Driven View of Disease Biology
Chapter 3: Small Molecules and Disease
Chapter 4: Protein Interactions and Disease
Chapter 5: Network Biology Approach to Complex Diseases
Chapter 6: Structural Variation and Medical Genomics
Chapter 7: Pharmacogenomics
Chapter 8: Biological Knowledge Assembly and Interpretation
Chapter 9: Analyses Using Disease Ontologies
Chapter 10: Mining Genome-Wide Genetic Markers
Chapter 11: Genome-Wide Association Studies
Chapter 12: Human Microbiome Analysis
Chapter 13: Mining Electronic Health Records in the Genomics Era
Chapter 14: Cancer Genome Analysis