Emerging pattern mining is a data mining task that aims to discover discriminative patterns,
which can describe emerging behavior with respect to a property of interest. In recent years …

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life
Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent …

Abstract Polyadenylation [poly (A)] is a vital step in post-transcriptional processing of pre-
mRNA. Alternative polyadenylation is a widespread mechanism of regulating gene …

In this paper, we introduce a new adaptive rule-based classifier for multi-class classification
of biological data, where several problems of classifying biological data are addressed …

Nowadays the amount of networks of devices and sensors, such as smart homes or smart
cities, is rapidly increasing. Each of these devices generates massive amounts of data on a …

In several problems, contrast pattern-based classifiers produce high accuracy and provide
an explanation of the result in terms of the patterns used for classification. However, class …

Abstract Background and objective Single Nucleotide Polymorphism (SNPs) are, nowadays,
becoming the marker of choice for biological analyses involving a wide range of applications …

Attribute Oriented Induction (AOI) produces highlevel characteristic summary data but does
not discover new emerging patterns. Emerging Pattern (EP) algorithms discover emerging …

Motivation: Polyadenylation is an essential process during eukaryotic gene expression.
Prediction of poly (A) sites helps to define the 3′ end of genes, which is important for gene …

In this review we highlight the importance of defining the untranslated parts of transcripts,
and present a number of computational approaches for the discovery and quantification of …