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The Characteristic Attribute Organization System (CAOS)
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maCAOS |
The Characteristic Attribute Organization System (CAOS) is a Columbia University Patent-Pending systematic method for discovering descriptive information about a priori organized discretized data. Preliminary work has shown applicability to the domains of Microarray Gene Chip analysis and Phylogenetic Inferencing analysis. Derived from some of the fundamental tenets from evolutionary biology, rule sets can be generated from data sets that can be used for effecicient and effective classification of novel data elements. For example, in the case of Microarray data, gene profiles can be determined that unambiguously classify new patients into disease groups (e.g., cancer vs. non-cancer). In the case of novel gene sequences, classification is possible using the descriptive rule sets that can aide in proper classification and, as a result, with correct nomenclature. Both of these issues are at the core of classification research. In addition to creating robust classification tools, CAOS descriptors can also drive scientific research. Through discovering characteristic gene patterns (in the case of Microarray analyses) or specific amino acids (in phylogenetic analyses) it may be possible to create specific experiments that can examine the biological meaning of said found characteristic descriptors. At present, the current implementation of the system has been designed for only a few specific cases. These specific cases were to demonstrate the efficacy and efficiency of the system, and have resulted in two peer-reviewed scientific publications. Further work on the system for usage in general research laboratories in research and commercial biological institutes will require a number of technical improvements to the current system. |
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