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: "Feature context-dependency and complexity-reduction in probability landscapes for integrative genomics | Theoretical Biology and Medical Modelling Research Feature context-dependency and complexity-reduction in 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 10 September 2008 Received 27 June 2008 Theoretical Biology and Medical Modelling 2008 5 21 doi 1742-4682-5-21 Accepted 10 September 2008 This article is available from http content 5 1 21 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 question of how to integrate heterogeneous sources of biological information into a coherent framework that allows the gene regulatory code in eukaryotes to be systematically investigated is one of the major challenges faced by systems biology. Probability landscapes which include as reference set the probabilistic representation of the genomic sequence have been proposed as a possible approach to the systematic discovery and analysis of correlations amongst initially heterogeneous and un-relatable descriptions and genome-wide measurements. Much of the available experimental sequence and genome activity information is de facto but not necessarily obviously context dependent. Furthermore the context dependency of the relevant information is itself dependent on the biological question addressed. It is hence necessary to develop a systematic way of discovering the context-dependency .
