Title
Practical approaches for mining frequent patterns in molecular datasets Practical approaches for mining frequent patterns in molecular datasets
Author
Faculty/Department
Faculty of Sciences. Biology
Faculty of Sciences. Chemistry
Faculty of Sciences. Mathematics and Computer Science
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
Subject
Chemistry
Biology
Human medicine
Computer. Automation
Source (journal)
Bioinformatics and biology insights. - -
Volume/pages
10(2016) , p. 37-47
ISSN
1177-9322
ISI
000376751000001
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Pattern detection is an inherent task in the analysis and interpretation of complex and continuously accumulating biological data. Numerous itemset mining algorithms have been developed in the last decade to efficiently detect specific pattern classes in data. Although many of these have proven their value for addressing bioinformatics problems, several factors still slow down promising algorithms from gaining popularity in the life science community. Many of these issues stem from the low user-friendliness of these tools and the complexity of their output, which is often large, static, and consequently hard to interpret. Here, we apply three software implementations on common bioinformatics problems and illustrate some of the advantages and disadvantages of each, as well as inherent pitfalls of biological data mining. Frequent itemset mining exists in many different flavors, and users should decide their software choice based on their research question, programming proficiency, and added value of extra features.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/212390/133097.pdf
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