@inproceedings{b18bb3bd2cc7471886de865b0babf2a2,
title = "Identification of the compound subjective rule interestingness measure for rule-based functional description of genes",
abstract = "Methods for automatic functional description of gene groups are useful tools supporting the interpretation of biological experiments. The RuleGO algorithm provides functional interpretation of gene groups in a form of logical rules including combinations of Gene Ontology terms in their premises. The number of rules generated by the algorithm is usually huge and additional methods of rule quality evaluation and filtration are required in order to select the most interesting ones. In the paper, we apply the multicriteria decision making UTA method to obtain a ranking of rules based on subjective expert opinion which is provided in a form of an ordered list of several rules. The presented approach is applied to the well known data set from microarray experiment and the results are compared with the standard RuleGO compound rule quality measure.",
keywords = "Gene Ontology, bioinformatics, functional annotations, multicriteria decision making, rule interestingness, rule quality",
author = "Aleksandra Gruca and Marek Sikora",
year = "2012",
doi = "10.1007/978-3-642-33185-5\_14",
language = "English",
isbn = "9783642331848",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "125--134",
booktitle = "Artificial Intelligence",
note = "15th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2012 ; Conference date: 12-09-2012 Through 15-09-2012",
}