Ahrens Christian H, Wagner Ulrich, Rehrauer Hubert K, Türker Can, Schlapbach Ralph
Functional Genomics Center Zurich, Winterthurerstrasse 190, Y32H66, CH-8057 Zurich, Switzerland.
EXS. 2007;97:277-307. doi: 10.1007/978-3-7643-7439-6_12.
Today's rapid development and broad application of high-throughput analytical technologies are transforming biological research and provide an amount of data and analytical opportunities to understand the fundamentals of biological processes undreamt of in past years. To fully exploit the potential of the large amount of data, scientists must be able to understand and interpret the information in an integrative manner. While the sheer data volume and heterogeneity of technical platforms within each discipline already poses a significant challenge, the heterogeneity of platforms and data formats across disciplines makes the integrative management, analysis, and interpretation of data a significantly more difficult task. This challenge thus lies at the heart of systems biology, which aims at a quantitative understanding of biological systems to the extent that systemic features can be predicted. In this chapter, we discuss several key issues that need to be addressed in order to put an integrated systems biology data analysis and mining within reach.
当今高通量分析技术的快速发展和广泛应用正在改变生物学研究,并提供了大量数据以及分析机会,以了解过去几年中未曾想象过的生物过程的基本原理。为了充分挖掘大量数据的潜力,科学家们必须能够以综合的方式理解和解释这些信息。虽然每个学科内技术平台的数据量庞大且具有异质性已经构成了重大挑战,但跨学科的平台和数据格式的异质性使得数据的综合管理、分析和解释成为一项更加艰巨的任务。因此,这一挑战是系统生物学的核心所在,系统生物学旨在对生物系统进行定量理解,以便能够预测系统特征。在本章中,我们将讨论为实现综合系统生物学数据分析和挖掘而需要解决的几个关键问题。