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Bioinform."],"abstract":"<jats:p><jats:bold>Introduction:<\/jats:bold> Association rule mining (ARM) is a powerful tool for exploring the informative relationships among multiple items (genes) in any dataset. The main problem of ARM is that it generates many rules containing different rule-informative values, which becomes a challenge for the user to choose the effective rules. In addition, few works have been performed on the integration of multiple biological datasets and variable cutoff values in ARM.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> To solve all these problems, in this article, we developed a novel framework <jats:italic>MOOVARM<\/jats:italic> (multi-objective optimized variable cutoff-based association rule mining) for multi-omics profiles.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> In this regard, we identified the positive ideal solution (<jats:italic>PIS<\/jats:italic>), which maximized the profit and minimized the loss, and negative ideal solution (<jats:italic>NIS<\/jats:italic>), which minimized the profit and maximized the loss for all gene sets (item sets), belonging to each extracted rule. Thereafter, we computed the distance (<jats:italic>d<\/jats:italic> +) from PIS and distance (<jats:italic>d<\/jats:italic> \u2212) from NIS for each gene set or product. These two distances played an important role in determining the optimized associations among various pairs of genes in the multi-omics dataset. We then globally estimated the relative closeness to <jats:italic>PIS<\/jats:italic> for ranking the gene sets. When the relative closeness score of the rule is greater than or equal to the pre-defined threshold value, the rule can be considered a final resultant rule. Moreover, <jats:italic>MOOVARM<\/jats:italic> evaluated the relative score of the rule based on the status of all genes instead of individual genes.<\/jats:p><jats:p><jats:bold>Conclusions:<\/jats:bold><jats:italic>MOOVARM<\/jats:italic> produced the final rank of the extracted (multi-objective optimized) rules of correlated genes which had better disease classification than the state-of-the-art algorithms on gene signature identification.<\/jats:p>","DOI":"10.3389\/fbinf.2023.1182176","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T03:03:55Z","timestamp":1690513435000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimal ranking and directional signature classification using the integral strategy of multi-objective optimization-based association rule mining of multi-omics data"],"prefix":"10.3389","volume":"3","author":[{"given":"Saurav","family":"Mallik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soumita","family":"Seth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amalendu","family":"Si","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tapas","family":"Bhadra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongming","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1145\/170035.170072","article-title":"Mining association rules between sets of items in large databases","volume-title":"Proc. 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