Promoter Sequences Prediction Using Relational Association Rule Mining

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

An Algorithm for the Discovery of Arbitrary Length Ordinal Association Rules

Abstract—Association rule mining techniques are used to search attribute-value pairs that occur frequently together in a data set. Ordinal association rules are a particular type of association rules that describe orderings between attributes that commonly occur over a data set [9]. Although ordinal association rules are defined between any number of the attributes, only discovery algorithms of binary ordinal association rules (i.e., rules between two attributes) exist. In this paper, we introduce the DOAR algorithm that efficiently finds all ordinal association rules of interest to the user, of any length, which hold over a data set. We present a theoretical validation of the algorithm and experimental results obtained by applying this algorithm on a real data set. I