基于集合的复杂性与生物信息
Set-based complexity and biological information
摘要：
在生命系统的分子和结构中存在的所有潜在信息中，哪一部分对系统是重要的或有意义的，这一点并不明显。例如，随机序列或相同重复序列的集合将被期望对细胞贡献很少或没有有用的信息。这个问题的定量信息是重要的，因为潮起潮落的生物学意义的信息是必不可少的，我们的定量了解生物功能和进化。具体由这些问题的生物信息的动机，我们在这里提出了一类措施，以量化的上下文性质的信息集的对象，基于柯尔莫哥洛夫的内在复杂性。这种措施折扣随机和冗余信息，是固有的，因为它们不需要一个定义的状态空间来量化的信息。这个新的措施，它可以制定在通用信息距离方面的最大化，似乎有几个有用的和有趣的属性，其中一些我们举例说明。
英文摘要：
It is not obvious what fraction of all the potential information residing in the molecules and structures of living systems is significant or meaningful to the system. Sets of random sequences or identically repeated sequences, for example, would be expected to contribute little or no useful information to a cell. This issue of quantitation of information is important since the ebb and flow of biologically significant information is essential to our quantitative understanding of biological function and evolution. Motivated specifically by these problems of biological information, we propose here a class of measures to quantify the contextual nature of the information in sets of objects, based on Kolmogorov's intrinsic complexity. Such measures discount both random and redundant information and are inherent in that they do not require a defined state space to quantify the information. The maximization of this new measure, which can be formulated in terms of the universal information distance, appears to have several useful and interesting properties, some of which we illustrate with examples.
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