Bioinformatics and Biomarker Discovery: "Omic" Data Analysis for Personalized Medicine

Preface

Biomarkers are indicators of disease occurrence and progression. Biomarkers can be used to predict clinical responses to treatments, and in some cases they may represent potential drug targets. Biomarkers can be derived from solid tissues and bio-fluids. Also they can refer to non-molecular risk or clinical factors, such as life-style information and physiological signals. Different types of biomarkers have been used in clinical practice to detect disease and predict clinical outcomes.

Advanced laboratory instruments and computing systems developed to decipher the structure and function of genes, proteins and other substances in the human body offer a great variety of imperfect yet potentially useful data. Such data can be used to describe systems and processes with diverse degrees of accuracy and uncertainty. These limitations and the complexity of biomedical problems represent natural obstacles to the idea of bringing new knowledge from the laboratory to the bedside.

The greatest challenge in biomarker discovery is not the discovery of powerful predictors of disease. Nor is it the design of sophisticated algorithms and tools. The greatest test is to demonstrate its potential relevance in a clinical setting. This requires strong evidence of improvements in the health or quality of life of patients. This also means that potential biomarkers should stand the challenge of independent validations and reproducibility of results.

Advances in this area have traditionally been ...