title: Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too.
abstract: In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.
authors: Computational Biology and Machine Learning Laboratory, Faculty of Medicine, Health and Life Sciences, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast Belfast, UK.
authors: Frank
authors: F
authors: Emmert-Streib
authors: Matthias
authors: M
authors: Dehmer
keywords: dynOmics data; genome medicine; high-throughput data; next-generation sequencing data; personalized medicine