期刊
IEEE TRANSACTIONS ON INFORMATION THEORY
卷 56, 期 2, 页码 667-677出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIT.2009.2037046
关键词
Complexity; criticality; information distance; networks; set complexity
资金
- NIH/NIGMS [R01-GM072855, P50-GM076547]
- NSF FIBR [EF-0527023]
- NIH [P50 GM076547, GM072855]
- Battelle Memorial Institute
- Academy of Finland [120411, 122973]
- American Cancer Society
- Academy of Finland (AKA) [122973, 122973, 120411, 120411] Funding Source: Academy of Finland (AKA)
The significant and meaningful fraction of all the potential information residing in the molecules and structures of living systems is unknown. Sets of random molecular 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, a class of measures is proposed 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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