George Tzanis

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At the end of the 1980's a new discipline, named data mining, emerged. The introduction of new technologies such as computers, satellites, new mass storage media and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of-often noisy-data efficiently, in an exploratory(More)
The prediction of the Translation Initiation Site (TIS) in a genomic sequence is an important issue in biological research. Although several methods have been proposed to deal with this problem, there is a great potential for the improvement of the accuracy of these methods. Due to various reasons, including noise in the data as well as biological reasons,(More)
The prediction of the translation initiation site (TIS) in a genomic sequence is an important issue in biological research. Several methods have been proposed to deal with it. However, it is still an open problem. In this paper we follow an approach consisting of a number of steps in order to increase TIS prediction accuracy. First, all the sequences are(More)
AbstrAct Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in(More)
Mining a transaction database for association rules is a particularly popular data mining task, which involves the search for frequent co-occurrences among items. One of the problems often encountered is the large number of weak rules extracted. Item taxonomies, when available, can be used to reduce them to a more usable volume. In this paper we introduce a(More)
In an mRNA sequence, the prediction of the exact codon where the process of translation starts (Translation Initiation Site – TIS) is a particularly important problem. So far it has been tackled by several researchers that apply various statistical and machine learning techniques, achieving high accuracy levels, often over 90%. In this paper we propose a(More)
This paper studies the problem of predicting future values for a number of water quality variables, based on measurements from underwater sensors. It performs both exploratory and automatic analysis of the collected data with a variety of linear and nonlinear modeling methods. The paper investigates issues, such as the ability to predict future values for a(More)
IntroductIon Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to(More)
This paper presents a study on polyadenylation site prediction, which is a very important problem in bioinformatics and medicine, promising to give a lot of answers especially in cancer research. We describe a method, called PolyA-iEP, that we developed for predicting polyadenylation sites and we present a systematic study of the problem of recognizing mRNA(More)