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ZENODO; Briefings in Bioinformatics
Article . 2013 . Peer-reviewed
License: CC BY
Data sources: ZENODO; Crossref
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A primer to frequent itemset mining for bioinformatics

Authors: Naulaerts, Stefan; Meysman, Pieter; Bittremieux, Wout; Vu, Trung N.; Vanden Berghe, Wim; Goethals, Bart; Laukens, Kris;

A primer to frequent itemset mining for bioinformatics

Abstract

Abstract: Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences.

Country
Belgium
Related Organizations
Subjects by Vocabulary

Microsoft Academic Graph classification: Association rule learning Computer science computer.software_genre Bioinformatics Biclustering Life Scientists Biological data Affinity analysis Data science Identification (information) Shopping basket Data mining computer

Keywords

biclustering, frequent item set, Polymorphism, Single Nucleotide, Pattern Recognition, Automated, association rule, Animals, Cluster Analysis, Data Mining, Humans, Gene Regulatory Networks, Biology, Molecular Biology, Computer. Automation, Gene Expression Profiling, Computational Biology, High-Throughput Nucleotide Sequencing, Chemistry, market basket analysis, Papers, Mathematics, Algorithms, Software, pattern mining, Information Systems

102 references, page 1 of 11

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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