{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:53:51Z","timestamp":1776394431454,"version":"3.51.2"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"22","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,11,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Association rule analysis methods are important techniques applied to gene expression data for finding expression relationships between genes. However, previous methods implicitly assume that all genes have similar importance, or they ignore the individual importance of each gene. The relation intensity between any two items has never been taken into consideration. Therefore, we proposed a technique named REMMAR (RElational-based Multiple Minimum supports Association Rules) algorithm to tackle this problem. This method adjusts the minimum relation support (MRS) for each gene pair depending on the regulatory relation intensity to discover more important association rules with stronger biological meaning.<\/jats:p>\n               <jats:p>Results: In the actual case study of this research, REMMAR utilized the shortest distance between any two genes in the Saccharomyces cerevisiae gene regulatory network (GRN) as the relation intensity to discover the association rules from two S.cerevisiae gene expression datasets. Under experimental evaluation, REMMAR can generate more rules with stronger relation intensity, and filter out rules without biological meaning in the protein\u2013protein interaction network (PPIN). Furthermore, the proposed method has a higher precision (100%) than the precision of reference Apriori method (87.5%) for the discovered rules use a literature survey. Therefore, the proposed REMMAR algorithm can discover stronger association rules in biological relationships dissimilated by traditional methods to assist biologists in complicated genetic exploration.<\/jats:p>\n               <jats:p>Availability: The source code in Java and other materials used in this study are available at http:\/\/websystem.csie.ncku.edu.tw\/REMMAR_Program.rar<\/jats:p>\n               <jats:p>Contact: \u00a0tsengsm@mail.ncku.edu.tw<\/jats:p>\n               <jats:p>Supplementary Information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr526","type":"journal-article","created":{"date-parts":[[2011,9,17]],"date-time":"2011-09-17T03:20:48Z","timestamp":1316229648000},"page":"3142-3148","source":"Crossref","is-referenced-by-count":24,"title":["Discovering relational-based association rules with multiple minimum supports on microarray datasets"],"prefix":"10.1093","volume":"27","author":[{"given":"Yu-Cheng","family":"Liu","sequence":"first","affiliation":[{"name":"1 Department of Computer Science and Information Engineering and 2Institute of Medical Informatics, National Cheng Kung University, Taiwan, R.O.C."}]},{"given":"Chun-Pei","family":"Cheng","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science and Information Engineering and 2Institute of Medical Informatics, National Cheng Kung University, Taiwan, R.O.C."}]},{"given":"Vincent S.","family":"Tseng","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science and Information Engineering and 2Institute of Medical Informatics, National Cheng Kung University, Taiwan, R.O.C."},{"name":"1 Department of Computer Science and Information Engineering and 2Institute of Medical Informatics, National Cheng Kung University, Taiwan, R.O.C."}]}],"member":"286","published-online":{"date-parts":[[2011,9,16]]},"reference":[{"key":"2023012511334138100_B1","first-page":"207","article-title":"Mining association rules between sets of items in large databases","volume-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data.","author":"Agrawal","year":"1993"},{"key":"2023012511334138100_B2","first-page":"487","article-title":"Fast algorithms for mining association rules","volume-title":"Proceedings of the 20th International Conference on Very Large Data Bases","author":"Agrawal","year":"1994"},{"key":"2023012511334138100_B3","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1093\/bib\/bbp042","article-title":"Gene association analysis: a survey of frequent pattern mining from gene expression data","volume":"11","author":"Alves","year":"2010","journal-title":"Brief. 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