tailieunhanh - Báo cáo y học: " Probability landscapes for integrative genomics"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: " Probability landscapes for integrative genomics | Theoretical Biology and Medical Modelling Research Probability landscapes for integrative genomics Annick Lesne1 and Arndt Benecke 1 2 BioMed Central Open Access Address 1Institut des Hautes Études Scientifiques Bures sur Yvette France and 2Institut de Recherche Interdisciplinaire - CNRS USR3078 -Université Lille I France Email Annick Lesne - lesne@ Arndt Benecke - arndt@ Corresponding author Published 20 May 2008 Received 28 February 2008 Theoretical Biology and Medical Modelling 2008 5 9 doi 1742-4682-5-9 Accepted 20 May 2008 This article is available from http content 5 1 9 2008 Lesne and Benecke licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background The comprehension of the gene regulatory code in eukaryotes is one of the major challenges of systems biology and is a requirement for the development of novel therapeutic strategies for multifactorial diseases. Its bi-fold degeneration precludes brute force and statistical approaches based on the genomic sequence alone. Rather recursive integration of systematic whole-genome experimental data with advanced statistical regulatory sequence predictions needs to be developed. Such experimental approaches as well as the prediction tools are only starting to become available and increasing numbers of genome sequences and empirical sequence annotations are under continual discovery-driven change. Furthermore given the complexity of the question a decade s long multi-laboratory effort needs to be envisioned. These constraints need to be considered in the creation of a framework that can pave a road to successful comprehension of the gene regulatory code. Results We introduce here a concept for such a framework based entirely on .