Promoter Sequences Prediction Using Relational Association Rule Mining

• Main Authors: Gabriela Czibula, Maria-Iuliana Bocicor, Istvan Gergely Czibula
• Format: Article
• Language: English
• Published: SAGE Publishing 2012-01-01
• Series: Evolutionary Bioinformatics
• Online Access: https://doi.org/10.4137/EBO.S9376

In this paper we are approaching, from a computational perspective, the problem of promoter sequences prediction, an important problem within the field of bioinformatics. As the conditions for a DNA sequence to function as a promoter are not known, machine learning based classification models are still developed to approach the problem of promoter identification in the DNA. We are proposing a classification model based on relational association rules mining. Relational association rules are a particular type of association rules and describe numerical orderings between attributes that commonly occur over a data set. Our classifier is based on the discovery of relational association rules for predicting if a DNA sequence contains or not a promoter region. An experimental evaluation of the proposed model and comparison with similar existing approaches is provided. The obtained results show that our classifier overperforms the existing techniques for identifying promoter sequences, confirming the potential of our proposal.