Abstract
BackgroundThe use of classification algorithms is becoming increasingly important for the field of computational biology. However, not only the quality of the classification, but also its biological interpretation is important. This interpretation may be eased if interacting elements can be identified and visualized, something that requires appropriate tools and methods.ResultsWe developed a new approach to detecting interactions in complex systems based on classification. Using rule-based classifiers, we previously proposed a rule network visualization strategy that may be applied as a heuristic for finding interactions. We now complement this work with Ciruvis, a web-based tool for the construction of rule networks from classifiers made of IF-THEN rules. Simulated and biological data served as an illustration of how the tool may be used to visualize and interpret classifiers. Furthermore, we used the rule networks to identify feature interactions, compared them to alternative methods, and computationally validated the findings.ConclusionsRule networks enable a fast method for model visualization and provide an exploratory heuristic to interaction detection. The tool is made freely available on the web and may thus be used to aid and improve rule-based classification.
Highlights
The use of classification algorithms is becoming increasingly important for the field of computational biology
Rule-based classifiers have earlier been applied to a wide spectrum of problems in genomics, proteomics, epigenetics, e.g., predict gene ontology terms from gene expression time profiles [7], to interpret microarray data [8], to model cleavage of polypeptide octamers by the HIV-1 protease [9], to model ligandreceptor interactions [10], and to classify Alzheimer’s patients [11]
The requirements on classification methods to be user friendly and easy to interpret have increased over the past years
Summary
The use of classification algorithms is becoming increasingly important for the field of computational biology. The quality of the classification, and its biological interpretation is important This interpretation may be eased if interacting elements can be identified and visualized, something that requires appropriate tools and methods. Rule-based classifiers are one type of classifiers Their strength lies in the fact that they are comparably easy to interpret while still producing models of reasonable quality, which have made them suitable for applications in systems biology. Rule-based classifiers have earlier been applied to a wide spectrum of problems in genomics, proteomics, epigenetics, e.g., predict gene ontology terms from gene expression time profiles [7], to interpret microarray data [8], to model cleavage of polypeptide octamers by the HIV-1 protease [9], to model ligandreceptor interactions [10], and to classify Alzheimer’s patients [11]
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