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GeneCodis3: a nonredundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res. 2012; 40:W478– W483. [PubMed: 22573175] Wolpin et al. Page 15 Nat Genet. Author manuscript; available
 in PMC 2015
"... Since its first release in 2007, GeneCodis has become a valuable tool to functionally interpret results from experimental techniques in genomics. This webbased application integrates different sources of information to finding groups of genes with similar biological meaning. This process, known as ..."
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Since its first release in 2007, GeneCodis has become a valuable tool to functionally interpret results from experimental techniques in genomics. This webbased application integrates different sources of information to finding groups of genes with similar biological meaning. This process, known as enrichment analysis, is essential in the interpretation of highthroughput experiments. The frequent feedbacks and the natural evolution of genomics and bioinformatics have allowed the growth of the tool and the development of this third release. In this version, a special effort has been made to remove noisy and redundant output from the enrichment results with the inclusion of a recently reported algorithm that summarizes significantly enriched terms and generates functionally coherent modules of genes and terms. A new comparative analysis has been added to allow the differential analysis of gene sets. To expand the scope of the application, new sources of biological information have been included, such as genetic diseases, drugs–genes interactions and Pubmed information among others. Finally, the graphic section has been renewed with the inclusion of new interactive graphics and filtering options. The application is freely available at
Comparing Performance of Formal Concept Analysis and Closed Frequent Itemset Mining Algorithms on Real Data
"... Abstract. In this paper, an experimental comparison of publicly available algorithms for computing intents of all formal concepts and mining frequent closed itemsets is provided. Experiments are performed on real data sets from UCI Machine Learning Repository and FIMI Repository. Results of experime ..."
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Abstract. In this paper, an experimental comparison of publicly available algorithms for computing intents of all formal concepts and mining frequent closed itemsets is provided. Experiments are performed on real data sets from UCI Machine Learning Repository and FIMI Repository. Results of experiments are discussed at the end of the paper. 1
Contrasting Subgroup Discovery
"... Subgroup discovery methods find interesting subsets of objects of a given class. Motivated by an application in bioinformatics, we first define a generalized subgroup discovery problem. In this setting, a subgroup is interesting if its members are characteristic for their class, even if the classes ..."
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Subgroup discovery methods find interesting subsets of objects of a given class. Motivated by an application in bioinformatics, we first define a generalized subgroup discovery problem. In this setting, a subgroup is interesting if its members are characteristic for their class, even if the classes are not identical. Then we further refine this setting for the case where subsets of objects, for example, subsets of objects that represent different time points or different phenotypes, are contrasted. We show that this allows finding subgroups of objects that could not be found with classical subgroup discovery. To find such subgroups, we propose an approach that consists of two subgroup discovery steps and an intermediate, contrast set definition step. This approach is applicable in various application areas. An example is biology, where interesting subgroups of genes are searched by using gene expression data. We address the problem of finding enriched gene sets that are specific for virus infected samples for a specific time point or a specific phenotype. We report on experimental results on a time series data set for virus infected Solanum tuberosum (potato) plants. The results on S. tuberosum’s response to virus infection revealed new research hypotheses for plant biologists.
Contrast Mining from Interesting Subgroups
"... Abstract. Subgroup discovery methods find interesting subsets of objects of a given class. We propose to extend subgroup discovery by a second subgroup discovery step to find interesting subgroups of objects specific for a class in one or more contrast classes. First, a subgroup discovery method is ..."
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Abstract. Subgroup discovery methods find interesting subsets of objects of a given class. We propose to extend subgroup discovery by a second subgroup discovery step to find interesting subgroups of objects specific for a class in one or more contrast classes. First, a subgroup discovery method is applied. Then, contrast classes of objects are defined by using set theoretic functions on the discovered subgroups of objects. Finally, subgroup discovery is performed to find interesting subgroups within the two contrast classes, pointing out differences between the characteristics of the two. This has various application areas, one being biology, where finding interesting subgroups has been addressed widely for geneexpression data. There, our method finds enriched gene sets which are common to samples in a class (e.g., differential expression in virus infected versus noninfected) and at the same time specific for one or more class attributes (e.g., time points or genotypes). We report on experimental results on a timeseries data set for virus infected potato plants. The results present a comprehensive overview of potato’s response to virus infection and reveal new research hypotheses for plant biologists. 1
doi:10.1093/comjnl/bxs132 Contrasting Subgroup Discovery
, 2011
"... Subgroup discovery methods find interesting subsets of objects of a given class. Motivated by an application in bioinformatics, we first define a generalized subgroup discovery problem. In this setting, a subgroup is interesting if its members are characteristic for their class, even if the classes ..."
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Subgroup discovery methods find interesting subsets of objects of a given class. Motivated by an application in bioinformatics, we first define a generalized subgroup discovery problem. In this setting, a subgroup is interesting if its members are characteristic for their class, even if the classes are not identical. Then we further refine this setting for the case where subsets of objects, for example, subsets of objects that represent different time points or different phenotypes, are contrasted. We show that this allows finding subgroups of objects that could not be found with classical subgroup discovery. To find such subgroups, we propose an approach that consists of two subgroup discovery steps and an intermediate, contrast set definition step. This approach is applicable in various application areas. An example is biology, where interesting subgroups of genes are searched by using gene expression data. We address the problem of finding enriched gene sets that are specific for virusinfected samples for a specific time point or a specific phenotype. We report on experimental results on a time series dataset for virusinfected Solanum tuberosum (potato) plants. The results on S. tuberosum’s response to virusinfection revealed new research hypotheses for plant biologists.
Efficient Mining of Association Rules in Oscillatorybased Data
"... Association rules are one of the most researched areas of data mining. Finding frequent patterns is an important step in association rules mining which is very time consuming and costly. In this paper, an effective method for mining association rules in the data with the oscillatory value (up, down) ..."
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Association rules are one of the most researched areas of data mining. Finding frequent patterns is an important step in association rules mining which is very time consuming and costly. In this paper, an effective method for mining association rules in the data with the oscillatory value (up, down) is presented, such as the stock price variation in stock exchange, which, just a few numbers of the counts of itemsets are searched from the database, and the counts of the rest of itemsets are computed using the relationships that exist between these types of data. Also, the strategy of pruning is used to decrease the searching space and increase the rate of the mining process. Thus, there is no need to investigate the entire frequent patterns from the database. This takes less time to find frequent patterns. By executing the MRMiner (an acronym for “Math RulesMiner”) algorithm, its performance on the real stock data is analyzed and shown. Our experiments show that the MRMiner algorithm can find association rules very efficiently in the data based on Oscillatory value type.
Advance Access publication on 22 September 2012 doi:10.1093/comjnl/bxs132 Contrasting Subgroup Discovery
, 2011
"... Subgroup discovery methods find interesting subsets of objects of a given class. Motivated by an application in bioinformatics, we first define a generalized subgroup discovery problem. In this setting, a subgroup is interesting if its members are characteristic for their class, even if the classes ..."
Abstract
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Subgroup discovery methods find interesting subsets of objects of a given class. Motivated by an application in bioinformatics, we first define a generalized subgroup discovery problem. In this setting, a subgroup is interesting if its members are characteristic for their class, even if the classes are not identical. Then we further refine this setting for the case where subsets of objects, for example, subsets of objects that represent different time points or different phenotypes, are contrasted. We show that this allows finding subgroups of objects that could not be found with classical subgroup discovery. To find such subgroups, we propose an approach that consists of two subgroup discovery steps and an intermediate, contrast set definition step. This approach is applicable in various application areas. An example is biology, where interesting subgroups of genes are searched by using gene expression data. We address the problem of finding enriched gene sets that are specific for virusinfected samples for a specific time point or a specific phenotype. We report on experimental results on a time series dataset for virusinfected Solanum tuberosum (potato) plants. The results on S. tuberosum’s response to virusinfection revealed new research hypotheses for plant biologists.