Journal article Open Access

A primer to frequent itemset mining for bioinformatics

Naulaerts, Stefan; Meysman, Pieter; Bittremieux, Wout; Vu, Trung Nghia; Vanden Berghe, Wim; Goethals, Bart; Laukens, Kris

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Naulaerts, Stefan</dc:creator>
  <dc:creator>Meysman, Pieter</dc:creator>
  <dc:creator>Bittremieux, Wout</dc:creator>
  <dc:creator>Vu, Trung Nghia</dc:creator>
  <dc:creator>Vanden Berghe, Wim</dc:creator>
  <dc:creator>Goethals, Bart</dc:creator>
  <dc:creator>Laukens, Kris</dc:creator>
  <dc:description>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.</dc:description>
  <dc:source>Briefings in Bioinformatics 16(2) 216–231</dc:source>
  <dc:title>A primer to frequent itemset mining for bioinformatics</dc:title>
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